Key concepts in clinical epidemiology: Research Integrity definitions and challenges.

Research integrity is guided by a set of principles to ensure research reliability, and rigor. It serves as a pillar to uphold society's trust in science and foster scientific progress. However, over the past two decades, a surge in research integrity concerns, including fraudulent research, reproducibility challenges, and questionable practices, has raised critical questions about the reliability of scientific outputs, particularly in biomedical research. In the biomedical sciences, any breaches in research integrity could potentially lead to a domino effect impacting patient care, medical interventions, and the broader implementation of healthcare policies. Addressing these breaches requires measures such as rigorous research methods, transparent reporting, and changing the research culture. Institutional support through clear guidelines, robust training, and mentorship is crucial to fostering a culture of research integrity. However, structural and institutional factors, including research incentives and recognition systems, play an important role in research behavior. Therefore, promoting research integrity demands a collective effort from all stakeholders to maintain public trust in the scientific community and ensure the reliability of science. Here we discuss some definitions and principles, the implications for biomedical sciences, and propose actionable steps to foster research integrity.

Methods used to develop the SPIRIT 2024 and CONSORT 2024 Statements.

OBJECTIVES: To describe, and explain the rationale for, the methods used and decisions made during development of the updated SPIRIT 2024 and CONSORT 2024 reporting guidelines. METHODS: We developed SPIRIT 2024 and CONSORT 2024 together to facilitate harmonization of the two guidelines, and incorporated content from key extensions. We conducted a scoping review of comments suggesting changes to SPIRIT 2013 and CONSORT 2010, and compiled a list of other possible revisions based on existing SPIRIT and CONSORT extensions, other reporting guidelines, and personal communications. From this, we generated a list of potential modifications or additions to SPIRIT and CONSORT, which we presented to stakeholders for feedback in an international online Delphi survey. The Delphi survey results were discussed at an online expert consensus meeting attended by 30 invited international participants. We then drafted the updated SPIRIT and CONSORT checklists and revised them based on further feedback from meeting attendees. RESULTS: We compiled 83 suggestions for revisions or additions to SPIRIT and/or CONSORT from the scoping review and 85 from other sources, from which we generated 33 potential changes to SPIRIT (n = 5) or CONSORT (n = 28). Of 463 participants invited to take part in the Delphi survey, 317 (68%) responded to Round 1, 303 (65%) to Round 2 and 290 (63%) to Round 3. Two additional potential checklist changes were added to the Delphi survey based on Round 1 comments. Overall, 14/35 (SPIRIT n = 0; CONSORT n = 14) proposed changes reached the predefined consensus threshold (≥80% agreement), and participants provided 3580 free-text comments. The consensus meeting participants agreed with implementing 11/14 of the proposed changes that reached consensus in the Delphi and supported implementing a further 4/21 changes (SPIRIT n = 2; CONSORT n = 2) that had not reached the Delphi threshold. They also recommended further changes to refine key concepts and for clarity. CONCLUSION: The forthcoming SPIRIT 2024 and CONSORT 2024 Statements will provide updated, harmonized guidance for reporting randomized controlled trial protocols and results, respectively. The simultaneous development of the SPIRIT and CONSORT checklists has been informed by current empirical evidence and extensive input from stakeholders. We hope that this report of the methods used will be helpful for developers of future reporting guidelines.

[Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionDiretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI]. / Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI.

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials ­ Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials ­ Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

A protocol for an overview of systematic reviews to map photodynamic inactivation evidence in different dental specialties.

This is a protocol for an overview to summarize the findings of Systematic Reviews (SR) dealing with Photodynamic Inactivation (PDI) for control of oral diseases. Specific variables of oral infectious will be considered as outcomes, according to dental specialty. Cochrane Database of Systematic Reviews (CDSR), MEDLINE, LILACS, Embase, and Epistemonikos will be searched, as well as reference lists. A search strategy was developed for each database using only terms related to the intervention (PDI) aiming to maximize sensitivity. After checking for duplicate entries, selection of reviews will be performed in a two-stage technique: two authors will independently screening titles and abstracts, and then full texts will be assessed for inclusion/exclusion criteria. Any disagreement will be resolved through discussion and/or consultation with a third reviewer. Data will be extracted following the recommendations in Chapter V of Cochrane Handbook and using an electronic pre-specified form. The evaluation of the methodological quality and risk of bias (RoB) of the SR included will be carried out using the AMSTAR 2 and ROBIS. Narrative summaries of relevant results from the individual SR will be carried out and displayed in tables and figures. A specific summary will focus on PDI parameters and study designs, such as the type and concentration of photosensitizer, pre-irradiation time, irradiation dosimetry, and infection or microbiological models, to identify the PDI protocols with clinical potential. We will summarize the quantitative results of the SRs narratively.

Informing the development of the SUCCEED reporting guideline for studies on the scaling of health interventions: A systematic review.

BACKGROUND: Quality reporting contributes to effective translation of health research in practice and policy. As an initial step in the development of a reporting guideline for scaling, the Standards for reporting stUdies of sCaling evidenCEd-informED interventions (SUCCEED), we performed a systematic review to identify relevant guidelines and compile a list of potential items. METHODS: We conducted a systematic review according to Cochrane method guidelines. We searched the following databases: MEDLINE, Embase, PsycINFO, Cochrane Library, CINAHL, Web of Science, from their respective inceptions. We also searched websites of relevant organizations and Google. We included any document that provided instructions or recommendations, e.g., reporting guideline, checklist, guidance, framework, standard; could inform the design or reporting of scaling interventions; and related to the health sector. We extracted characteristics of the included guidelines and assessed their methodological quality using a 3-item internal validity assessment tool. We extracted all items from the guidelines and classified them according to the main sections of reporting guidelines (title, abstract, introduction, methods, results, discussion and other information). We performed a narrative synthesis based on descriptive statistics. RESULTS: Of 7704 records screened (published between 1999 and 2019), we included 39 guidelines, from which data were extracted from 57 reports. Of the 39 guidelines, 17 were for designing scaling interventions and 22 for reporting implementation interventions. At least one female author was listed in 31 guidelines, and 21 first authors were female. None of the authors belonged to the patient stakeholder group. Only one guideline clearly identified a patient as having participated in the consensus process. More than half the guidelines (56%) had been developed using an evidence-based process. In total, 750 items were extracted from the 39 guidelines and distributed into the 7 main sections. CONCLUSION: Relevant items identified could inform the development of a reporting guideline for scaling studies of evidence-based health interventions. This and our assessment of guidelines could contribute to better reporting in the science and practice of scaling.

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare.

The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM is a patient-centric approach that combines conventional medicine with complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making and personalized treatment plans. This article explores how AI technologies can complement and enhance TCIM, aligning with the shared objectives of researchers from both fields in improving patient outcomes, enhancing care quality, and promoting holistic wellness. This integration of TCIM and AI introduces exciting opportunities but also noteworthy challenges. AI may augment TCIM by assisting in early disease detection, providing personalized treatment plans, predicting health trends, and enhancing patient engagement. Challenges at the intersection of AI and TCIM include data privacy and security, regulatory complexities, maintaining the human touch in patient-provider relationships, and mitigating bias in AI algorithms. Patients' trust, informed consent, and legal accountability are all essential considerations. Future directions in AI-enhanced TCIM include advanced personalized medicine, understanding the efficacy of herbal remedies, and studying patient-provider interactions. Research on bias mitigation, patient acceptance, and trust in AI-driven TCIM healthcare is crucial. In this article, we outlined that the merging of TCIM and AI holds great promise in enhancing healthcare delivery, personalizing treatment plans, preventive care, and patient engagement. Addressing challenges and fostering collaboration between AI experts, TCIM practitioners, and policymakers, however, is vital to harnessing the full potential of this integration.

Methods proposed for monitoring the implementation of evidence-based research: a cross-sectional study.

OBJECTIVES: Evidence-based research (EBR) is the systematic and transparent use of prior research to inform a new study so that it answers questions that matter in a valid, efficient, and accessible manner. This study surveyed experts about existing (e.g., citation analysis) and new methods for monitoring EBR and collected ideas about implementing these methods. STUDY DESIGN AND SETTING: We conducted a cross-sectional study via an online survey between November 2022 and March 2023. Participants were experts from the fields of evidence synthesis and research methodology in health research. Open-ended questions were coded by recurring themes; descriptive statistics were used for quantitative questions. RESULTS: Twenty-eight expert participants suggested that citation analysis should be supplemented with content evaluation (not just what is cited but also in which context), content expert involvement, and assessment of the quality of cited systematic reviews. They also suggested that citation analysis could be facilitated with automation tools. They emphasized that EBR monitoring should be conducted by ethics committees and funding bodies before the research starts. Challenges identified for EBR implementation monitoring were resource constraints and clarity on responsibility for EBR monitoring. CONCLUSION: Ideas proposed in this study for monitoring the implementation of EBR can be used to refine methods and define responsibility but should be further explored in terms of feasibility and acceptability. Different methods may be needed to determine if the use of EBR is improving over time.

Cardiology researchers' practices and perceived barriers to open science: an international survey.

OBJECTIVE: Open science is a movement and set of practices to conduct research more transparently. Implementing open science will significantly improve public access and supports equity. It also has the potential to foster innovation and reduce duplication through data and materials sharing. Here, we survey an international group of researchers publishing in cardiovascular journals regarding their perceptions and practices related to open science. METHODS: We identified the top 100 'Cardiology and Cardiovascular Medicine' subject category journals from the SCImago journal ranking platform. This is a publicly available portal that draws from Scopus. We then extracted the corresponding author's name and email from all articles published in these journals between 1 March 2021 and 1 March 2022. Participants were sent a purpose-built survey about open science. The survey contained primarily multiple choice and scale-based questions for which we report count data and percentages. For the few text-based responses we conducted thematic content analysis. RESULTS: 198 participants responded to our survey. Participants had a mean response of 6.8 (N=197, SD=1.8) on a 9-point scale with endpoints, not at all familiar (1) and extremely familiar (9), when indicating how familiar they were with open science. When asked about where they obtained open science training, most participants indicated this was done on the job self-initiated while conducting research (n=103, 52%), or that they had no formal training with respect to open science (n=72, 36%). More than half of the participants indicated they would benefit from practical support from their institution on how to perform open science practices (N=106, 54%). A diversity of barriers to each of the open science practices presented to participants were acknowledged. Participants indicated that funding was the most essential incentive to adopt open science. CONCLUSIONS: It is clear that policy alone will not lead to the effective implementation of open science. This survey serves as a baseline for the cardiovascular research community's open science performance and perception and can be used to inform future interventions and monitoring.

Screening for depression in children and adolescents in primary care or non-mental health settings: a systematic review update.

BACKGROUND: The transition from childhood to adolescence is associated with an increase in rates of some psychiatric disorders, including major depressive disorder, a debilitating mood disorder. The aim of this systematic review is to update the evidence on the benefits and harms of screening for depression in primary care and non-mental health clinic settings among children and adolescents. METHODS: This review is an update of a previous systematic review, for which the last search was conducted in 2017. We searched Ovid MEDLINE® ALL, Embase Classic+Embase, PsycINFO, Cochrane Central Register of Controlled Trials, and CINAHL on November 4, 2019, and updated on February 19, 2021. If no randomized controlled trials were found, we planned to conduct an additional search for non-randomized trials with a comparator group. For non-randomized trials, we applied a non-randomized controlled trial filter and searched the same databases except for Cochrane Central Register of Controlled Trials from January 2015 to February 2021. We also conducted a targeted search of the gray literature for unpublished documents. Title and abstract, and full-text screening were completed independently by pairs of reviewers. RESULTS: In this review update, we were unable to find any randomized controlled studies that satisfied our eligibility criteria and evaluated the potential benefits and harms of screening for depression in children and adolescents. Additionally, a search for non-randomized trials yielded no studies that met the inclusion criteria. CONCLUSIONS: The findings of this review indicate a lack of available evidence regarding the potential benefits and harms of screening for depression in children and adolescents. This absence of evidence emphasizes the necessity for well-conducted clinical trials to evaluate the effectiveness of depression screening among children and adolescents in primary care and non-mental health clinic settings. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020150373 .

Guidelines for reporting pediatric and child health clinical trial protocols and reports: study protocol for SPIRIT-Children and CONSORT-Children.

BACKGROUND: Despite the critical importance of clinical trials to provide evidence about the effects of intervention for children and youth, a paucity of published high-quality pediatric clinical trials persists. Sub-optimal reporting of key trial elements necessary to critically appraise and synthesize findings is prevalent. To harmonize and provide guidance for reporting in pediatric controlled clinical trial protocols and reports, reporting guideline extensions to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) and Consolidated Standards of Reporting Trials (CONSORT) guidelines specific to pediatrics are being developed: SPIRIT-Children (SPIRIT-C) and CONSORT-Children (CONSORT-C). METHODS: The development of SPIRIT-C/CONSORT-C will be informed by the Enhancing the Quality and Transparency of Health Research Quality (EQUATOR) method for reporting guideline development in the following stages: (1) generation of a preliminary list of candidate items, informed by (a) items developed during initial development efforts and child relevant items from recent published SPIRIT and CONSORT extensions; (b) two systematic reviews and environmental scan of the literature; (c) workshops with young people; (2) an international Delphi study, where a wide range of panelists will vote on the inclusion or exclusion of candidate items on a nine-point Likert scale; (3) a consensus meeting to discuss items that have not reached consensus in the Delphi study and to "lock" the checklist items; (4) pilot testing of items and definitions to ensure that they are understandable, useful, and applicable; and (5) a final project meeting to discuss each item in the context of pilot test results. Key partners, including young people (ages 12-24 years) and family caregivers (e.g., parents) with lived experiences with pediatric clinical trials, and individuals with expertise and involvement in pediatric trials will be involved throughout the project. SPIRIT-C/CONSORT-C will be disseminated through publications, academic conferences, and endorsement by pediatric journals and relevant research networks and organizations. DISCUSSION: SPIRIT/CONSORT-C may serve as resources to facilitate comprehensive reporting needed to understand pediatric clinical trial protocols and reports, which may improve transparency within pediatric clinical trials and reduce research waste. TRIAL REGISTRATION: The development of these reporting guidelines is registered with the EQUATOR Network: SPIRIT-Children ( https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-clinical-trials-protocols/#35 ) and CONSORT-Children ( https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-clinical-trials/#CHILD ).

Screening for hypertension in adults: protocol for evidence reviews to inform a Canadian Task Force on Preventive Health Care guideline update.

PURPOSE: To inform updated recommendations by the Canadian Task Force on Preventive Health Care on screening in a primary care setting for hypertension in adults aged 18 years and older. This protocol outlines the scope and methods for a series of systematic reviews and one overview of reviews. METHODS: To evaluate the benefits and harms of screening for hypertension, the Task Force will rely on the relevant key questions from the 2021 United States Preventive Services Task Force systematic review. In addition, a series of reviews will be conducted to identify, appraise, and synthesize the evidence on (1) the association of blood pressure measurement methods and future cardiovascular (CVD)-related outcomes, (2) thresholds for discussions of treatment initiation, and (3) patient acceptability of hypertension screening methods. For the review of blood pressure measurement methods and future CVD-related outcomes, we will perform a de novo review and search MEDLINE, Embase, CENTRAL, and APA PsycInfo for randomized controlled trials, prospective or retrospective cohort studies, nested case-control studies, and within-arm analyses of intervention studies. For the thresholds for discussions of treatment initiation review, we will perform an overview of reviews and update results from a relevant 2019 UK NICE review. We will search MEDLINE, Embase, APA PsycInfo, and Epistemonikos for systematic reviews. For the acceptability review, we will perform a de novo systematic review and search MEDLINE, Embase, and APA PsycInfo for randomized controlled trials, controlled clinical trials, and observational studies with comparison groups. Websites of relevant organizations, gray literature sources, and the reference lists of included studies and reviews will be hand-searched. Title and abstract screening will be completed by two independent reviewers. Full-text screening, data extraction, risk-of-bias assessment, and GRADE (Grading of Recommendations Assessment, Development and Evaluation) will be completed independently by two reviewers. Results from included studies will be synthesized narratively and pooled via meta-analysis when appropriate. The GRADE approach will be used to assess the certainty of evidence for outcomes. DISCUSSION: The results of the evidence reviews will be used to inform Canadian recommendations on screening for hypertension in adults aged 18 years and older. SYSTEMATIC REVIEW REGISTRATION: This protocol is registered on PROSPERO and is available on the Open Science Framework (osf.io/8w4tz).

A Decade of Efforts to Add Value to Child Health Research Practices.

OBJECTIVE: To identify practices that add value to improve the design, conduct, and reporting of child health research and reduce research waste. STUDY DESIGN: In order to categorize the contributions of members of Standards for Research (StaR) in Child Health network, we developed a novel Child Health Improving Research Practices (CHIRP) framework comprised of 5 domains meant to counteract avoidable child health research waste and improve quality: 1) address research questions relevant to children, their families, clinicians, and researchers; 2) apply appropriate research design, conduct and analysis; 3) ensure efficient research oversight and regulation; 4) Provide accessible research protocols and reports; and 5) develop unbiased and usable research reports, including 17 responsible research practice recommendations. All child health research relevant publications by the 48 original StaR standards' authors over the last decade were identified, and main topic areas were categorized using this framework. RESULTS: A total of 247 publications were included in the final sample: 100 publications (41%) in domain 1 (3 recommendations), 77 publications (31%) in domain 2 (3), 35 publications (14%) in domain 3 (4), 20 publications (8%) in domain 4 (4), and 15 publications (6%) in domain 5 (3). We identified readily implementable "responsible" research practices to counter child health research waste and improve quality, especially in the areas of patients and families' engagement throughout the research process, developing Core Outcome Sets, and addressing ethics and regulatory oversight issues. CONCLUSION: While most of the practices are readily implementable, increased awareness of methodological issues and wider guideline uptake is needed to improve child health research. The CHIRP Framework can be used to guide responsible research practices that add value to child health research.

SARS-CoV-2 transmission risk for common group activities and settings: a living scoping review.

BACKGROUND: While the modes of transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are well studied, the risk of transmission in various group settings or activities is less clear. This living scoping review aims to summarize the risk factors of coronavirus disease 2019 (COVID-19) spread in common group activities (e.g. social gatherings) or settings (e.g. schools, hospitals, shared workplaces) to understand the drivers of transmission and to inform a risk assessment profile tool for use of rapid antigen detection tests. METHODS: We systematically searched electronic databases, MEDLINE and Embase, from January 2019 until February 2022. We included studies that evaluated the risk of SARS-CoV-2 transmission in activities and settings, deemed strategically important to government departments in Ireland, provided by the Department of Health (Ireland) Expert Advisory Group on Rapid Testing. RESULTS: After screening 14 052 records, data from 139 studies were narratively synthesized. The risk was consistently reported as 'high' for large social events (e.g. weddings) and indoor sports, working in healthcare settings and shared workplaces, working/living in residential settings and travelling via public transportation. Most studies were from healthcare settings, with common risk factors including close contact with COVID-19 cases, working in high-risk departments and inappropriate use of personal protective equipment. For other settings and activities, lack of infection prevention and control practices reportedly contributed to infection transmission. CONCLUSION: The heterogeneity across studies and lack of direct information on dominant variants, preventive measures, vaccination coverage necessitates further research on transmission risk within group activities to inform infection prevention and control measures.

Transparency, openness, and reproducible research practices are frequently underused in health economic evaluations.

OBJECTIVES: To investigate the extent to which articles of economic evaluations of healthcare interventions indexed in MEDLINE incorporate research practices that promote transparency, openness, and reproducibility. STUDY DESIGN AND SETTING: We evaluated a random sample of health economic evaluations indexed in MEDLINE during 2019. We included articles written in English reporting an incremental cost-effectiveness ratio in terms of costs per life years gained, quality-adjusted life years, and/or disability-adjusted life years. Reproducible research practices, openness, and transparency in each article were extracted in duplicate. We explored whether reproducible research practices were associated with self-report use of a guideline. RESULTS: We included 200 studies published in 147 journals. Almost half were published as open access articles (n = 93; 47%). Most studies (n = 150; 75%) were model-based economic evaluations. In 109 (55%) studies, authors self-reported use a guideline (e.g., for study conduct or reporting). Few studies (n = 31; 16%) reported working from a protocol. In 112 (56%) studies, authors reported the data needed to recreate the incremental cost-effectiveness ratio for the base case analysis. This percentage was higher in studies using a guideline than studies not using a guideline (72/109 [66%] with guideline vs. 40/91 [44%] without guideline; risk ratio 1.50, 95% confidence interval 1.15-1.97). Only 10 (5%) studies mentioned access to raw data and analytic code for reanalyses. CONCLUSION: Transparency, openness, and reproducible research practices are frequently underused in health economic evaluations. This study provides baseline data to compare future progress in the field.

Evidence Synthesis for Complex Interventions Using Meta-Regression Models.

A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data.

Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI / Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension / Diretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI

resumen está disponible en el texto completo

ABSTRACT The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

RESUMO A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.

Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI / Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension / Diretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI

resumen está disponible en el texto completo

ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.

Systematic review search strategies are poorly reported and not reproducible: a cross-sectional metaresearch study.

OBJECTIVES: To determine the reproducibility of biomedical systematic review search strategies. STUDY DESIGN AND SETTING: A cross-sectional reproducibility study was conducted on a random sample of 100 systematic reviews indexed in MEDLINE in November 2021. The primary outcome measure is the percentage of systematic reviews for which all database searches can be reproduced, operationalized as fulfilling six key Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension (PRISMA-S) reporting guideline items and having all database searches reproduced within 10% of the number of original results. Key reporting guideline items included database name, multi-database searching, full search strategies, limits and restrictions, date(s) of searches, and total records. RESULTS: The 100 systematic review articles contained 453 database searches. Only 22 (4.9%) database searches reported all six PRISMA-S items. Forty-seven (10.4%) database searches could be reproduced within 10% of the number of results from the original search; six searches differed by more than 1,000% between the originally reported number of results and the reproduction. Only one systematic review article provided the necessary search details to be fully reproducible. CONCLUSION: Systematic review search reporting is poor. To correct this will require a multifaceted response from authors, peer reviewers, journal editors, and database providers.

Cross-Sectional Evaluation of Open Science Practices at Imaging Journals: A Meta-Research Study.

Objective: To evaluate open science policies of imaging journals, and compliance to these policies in published articles. Methods: From imaging journals listed we extracted open science policy details: protocol registration, reporting guidelines, funding, ethics and conflicts of interest (COI), data sharing, and open access publishing. The 10 most recently published studies from each journal were assessed to determine adherence to these policies. We calculated the proportion of open science policies into an Open Science Score (OSS) for all journals and articles. We evaluated relationships between OSS and journal/article level variables. Results: 82 journals/820 articles were included. The OSS of journals and articles was 58.3% and 31.8%, respectively. Of the journals, 65.9% had registration and 78.1% had reporting guideline policies. 79.3% of journals were members of COPE, 81.7% had plagiarism policies, 100% required disclosure of funding, and 97.6% required disclosure of COI and ethics approval. 81.7% had data sharing policies and 15.9% were fully open access. 7.8% of articles had a registered protocol, 8.4% followed a reporting guideline, 77.4% disclosed funding, 88.7% disclosed COI, and 85.6% reported ethics approval. 12.3% of articles shared their data. 51% of articles were available through open access or as a preprint. OSS was higher for journal with DOAJ membership (80% vs 54.2%; P < .0001). Impact factor was not correlated with journal OSS. Knowledge synthesis articles has a higher OSS scores (44.5%) than prospective/retrospective studies (32.6%, 30.0%, P < .0001). Conclusion: Imaging journals endorsed just over half of open science practices considered; however, the application of these practices at the article level was lower.