The NICE search filters for treating and managing COVID-19: validation in MEDLINE and Embase (Ovid).

Objective: In this paper we report how the United Kingdom's National Institute for Health and Care Excellence (NICE) search filters for treating and managing COVID-19 were validated for use in MEDLINE (Ovid) and Embase (Ovid). The objective was to achieve at least 98.9% for recall and 64% for precision. Methods: We did two tests of recall to finalize the draft search filters. We updated the data from an earlier peer-reviewed publication for the first recall test. For the second test, we collated a set of systematic reviews from Epistemonikos COVID-19 L.OVE and extracted their primary studies. We calculated precision by screening all the results retrieved by the draft search filters from a targeted sample covering 2020-23. We developed a gold-standard set to validate the search filter by using all articles available from the "Treatment and Management" subject filter in the Cochrane COVID-19 Study Register. Results: In the first recall test, both filters had 99.5% recall. In the second test, recall was 99.7% and 99.8% in MEDLINE and Embase respectively. Precision was 91.1% in a deduplicated sample of records. In validation, we found the MEDLINE filter had recall of 99.86% of the 14,625 records in the gold-standard set. The Embase filter had 99.88% recall of 19,371 records. Conclusion: We have validated search filters to identify records on treating and managing COVID-19. The filters may require subsequent updates, if new SARS-CoV-2 variants of concern or interest are discussed in future literature.

MEDLINE citation tool accuracy: an analysis in two platforms.

Background: Libraries provide access to databases with auto-cite features embedded into the services; however, the accuracy of these auto-cite buttons is not very high in humanities and social sciences databases. Case Presentation: This case compares two biomedical databases, Ovid MEDLINE and PubMed, to see if either is reliable enough to confidently recommend to students for use when writing papers. A total of 60 citations were assessed, 30 citations from each citation generator, based on the top 30 articles in PubMed from 2010 to 2020. Conclusions: Error rates were higher in Ovid MEDLINE than PubMed but neither database platform provided error-free references. The auto-cite tools were not reliable. Zero of the 60 citations examined were 100% correct. Librarians should continue to advise students not to rely solely upon citation generators in these biomedical databases.

Bridging the gap in author names: building an enhanced author name dataset for biomedical literature system.

OBJECTIVE: Author name incompleteness, referring to only first initial available instead of full first name, is a long-standing problem in MEDLINE and has a negative impact on biomedical literature systems. The purpose of this study is to create an Enhanced Author Names (EAN) dataset for MEDLINE that maximizes the number of complete author names. MATERIALS AND METHODS: The EAN dataset is built based on a large-scale name comparison and restoration with author names collected from multiple literature databases such as MEDLINE, Microsoft Academic Graph, and Semantic Scholar. We assess the impact of EAN on biomedical literature systems by conducting comparative and statistical analyses between EAN and MEDLINE's author names dataset (MAN) on 2 important tasks, author name search and author name disambiguation. RESULTS: Evaluation results show that EAN improves the number of full author names in MEDLINE from 69.73 million to 110.9 million. EAN not only restores a substantial number of abbreviated names prior to the year 2002 when the NLM changed its author name indexing policy but also improves the availability of full author names in articles published afterward. The evaluation of the author name search and author name disambiguation tasks reveal that EAN is able to significantly enhance both tasks compared to MAN. CONCLUSION: The extensive coverage of full names in EAN suggests that the name incompleteness issue can be largely mitigated. This has significant implications for the development of an improved biomedical literature system. EAN is available at https://zenodo.org/record/10251358, and an updated version is available at https://zenodo.org/records/10663234.

Influence of automated indexing in Medical Subject Headings (MeSH) selection for pharmacy practice journals.

BACKGROUND: The Medical Subject Headings (MeSH) thesaurus is the controlled vocabulary used to index articles in MEDLINE. MeSH were mainly manually selected until June 2022 when an automated algorithm, the Medical Text Indexer (MTI) automated was fully implemented. A selection of automated indexed articles is then reviewed (curated) by human indexers to ensure the quality of the process. OBJECTIVE: To describe the association of MEDLINE indexing methods (i.e., manual, automated, and automated + curated) on the MeSH assignment in pharmacy practice journals compared with medical journals. METHODS: Original research articles published between 2016 and 2023 in two groups of journals (i.e., the Big-five general medicine and three pharmacy practice journals) were selected from PubMed using journal-specific search strategies. Metadata of the articles, including MeSH terms and indexing method, was extracted. A list of pharmacy-specific MeSH terms had been compiled from previously published studies, and their presence in pharmacy practice journal records was investigated. Using bivariate and multivariate analyses, as well as effect size measures, the number of MeSH per article was compared between journal groups, geographic origin of the journal, and indexing method. RESULTS: A total of 8479 original research articles was retrieved: 6254 from the medical journals and 2225 from pharmacy practice journals. The number of articles indexed by the various methods was disproportionate; 77.8 % of medical and 50.5 % of pharmacy manually indexed. Among those indexed using the automated system, 51.1 % medical and 10.9 % pharmacy practice articles were then curated to ensure the indexing quality. Number of MeSH per article varied among the three indexing methods for medical and pharmacy journals, with 15.5 vs. 13.0 in manually indexed, 9.4 vs. 7.4 in automated indexed, and 12.1 vs. 7.8 in automated and then curated, respectively. Multivariate analysis showed significant effect of indexing method and journal group in the number of MeSH attributed, but not the geographical origin of the journal. CONCLUSIONS: Articles indexed using automated MTI have less MeSH than manually indexed articles. Articles published in pharmacy practice journals were indexed with fewer number of MeSH compared with general medical journal articles regardless of the indexing method used.

Enhancing the coverage of SemRep using a relation classification approach.

OBJECTIVE: Relation extraction is an essential task in the field of biomedical literature mining and offers significant benefits for various downstream applications, including database curation, drug repurposing, and literature-based discovery. The broad-coverage natural language processing (NLP) tool SemRep has established a solid baseline for extracting subject-predicate-object triples from biomedical text and has served as the backbone of the Semantic MEDLINE Database (SemMedDB), a PubMed-scale repository of semantic triples. While SemRep achieves reasonable precision (0.69), its recall is relatively low (0.42). In this study, we aimed to enhance SemRep using a relation classification approach, in order to eventually increase the size and the utility of SemMedDB. METHODS: We combined and extended existing SemRep evaluation datasets to generate training data. We leveraged the pre-trained PubMedBERT model, enhancing it through additional contrastive pre-training and fine-tuning. We experimented with three entity representations: mentions, semantic types, and semantic groups. We evaluated the model performance on a portion of the SemRep Gold Standard dataset and compared it to SemRep performance. We also assessed the effect of the model on a larger set of 12K randomly selected PubMed abstracts. RESULTS: Our results show that the best model yields a precision of 0.62, recall of 0.81, and F1 score of 0.70. Assessment on 12K abstracts shows that the model could double the size of SemMedDB, when applied to entire PubMed. We also manually assessed the quality of 506 triples predicted by the model that SemRep had not previously identified, and found that 67% of these triples were correct. CONCLUSION: These findings underscore the promise of our model in achieving a more comprehensive coverage of relationships mentioned in biomedical literature, thereby showing its potential in enhancing various downstream applications of biomedical literature mining. Data and code related to this study are available at https://github.com/Michelle-Mings/SemRep_RelationClassification.

Using word evolution to predict drug repurposing.

BACKGROUND: Traditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing. RESULTS: Word embeddings, which represent a word's context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug's repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE. CONCLUSIONS: The annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique's suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach.

Palliative care needs of people and/or their families with serious and/or chronic health conditions in low- or middle-income country (LMIC) humanitarian settings-a systematic scoping review protocol.

BACKGROUND: Palliative care in low- or middle-income country (LMIC) humanitarian settings is a new area, experiencing a degree of increased momentum over recent years. The review contributes to this growing body of knowledge, in addition to identifying gaps for future research. The overall aim is to systematically explore the evidence on palliative care needs of patients and/or their families in LMIC humanitarian settings. METHODS: Arksey and O'Malley's (Int J Soc Res Methodol. 8:19-32, 2005) scoping review framework forms the basis of the study design, following further guidance from Levac et al. (Implement Sci 5:1-9, 2010), the Joanna Briggs Institute (JBI) Peters et al. (JBI Reviewer's Manual JBI: 406-452, 2020), and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) from Tricco et al. (Ann Intern Med 169:467-73, 2018). This incorporates a five-step approach and the population, concept, and context (PCC) framework. Using already identified key words/terms, searches for both published research and gray literature from January 2012 to October 2022 will be undertaken using databases (likely to include Cumulative Index of Nursing and Allied Health (CINAHL), MEDLINE, Embase, Global Health, Scopus, Applied Social Science Index and Abstracts (ASSIA), Web of Science, Policy Commons, JSTOR, Library Network International Monetary Fund and World Bank, Google Advanced Search, and Google Scholar) in addition to selected pre-print sites and websites. Data selection will be undertaken based on the inclusion and exclusion criteria and will be reviewed at each stage by two reviewers, with a third to resolve any differences. Extracted data will be charted in a table. Ethical approval is not required for this review. DISCUSSION: Findings will be presented in tables and diagrams/charts, followed by a narrative description. The review will run from late October 2022 to early 2023. This is the first systematic scoping review specifically exploring the palliative care needs of patients and/or their family, in LMIC humanitarian settings. The paper from the review findings will be submitted for publication in 2023.

A computable biomedical knowledge system: Toward rapidly building candidate-directed acyclic graphs.

AIM: It is essential for health researchers to have a systematic understanding of third-party variables that influence both the exposure and outcome under investigation, as shown by a directed acyclic graph (DAG). The traditional construction of DAGs through literature review and expert knowledge often needs to be more systematic and consistent, leading to potential biases. We try to introduce an automatic approach to building network linking variables of interest. METHODS: Large-scale text mining from medical literature was utilized to construct a conceptual network based on the Semantic MEDLINE Database (SemMedDB). SemMedDB is a PubMed-scale repository of the "concept-relation-concept" triple format. Relations between concepts are categorized as Excitatory, Inhibitory, or General. RESULTS: To facilitate the use of large-scale triple sets in SemMedDB, we have developed a computable biomedical knowledge (CBK) system (https://cbk.bjmu.edu.cn/), a website that enables direct retrieval of related publications and their corresponding triples without the necessity of writing SQL statements. Three case studies were elaborated to demonstrate the applications of the CBK system. CONCLUSIONS: The CBK system is openly available and user-friendly for rapidly capturing a set of influencing factors for a phenotype and building candidate DAGs between exposure-outcome variables. It could be a valuable tool to reduce the exploration time in considering relationships between variables, and constructing a DAG. A reliable and standardized DAG could significantly improve the design and interpretation of observational health research.

Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed.

BACKGROUND: Interrupted time series (ITS) studies contribute importantly to systematic reviews of population-level interventions. We aimed to develop and validate search filters to retrieve ITS studies in MEDLINE and PubMed. METHODS: A total of 1017 known ITS studies (published 2013-2017) were analysed using text mining to generate candidate terms. A control set of 1398 time-series studies were used to select differentiating terms. Various combinations of candidate terms were iteratively tested to generate three search filters. An independent set of 700 ITS studies was used to validate the filters' sensitivities. The filters were test-run in Ovid MEDLINE and the records randomly screened for ITS studies to determine their precision. Finally, all MEDLINE filters were translated to PubMed format and their sensitivities in PubMed were estimated. RESULTS: Three search filters were created in MEDLINE: a precision-maximising filter with high precision (78%; 95% CI 74%-82%) but moderate sensitivity (63%; 59%-66%), most appropriate when there are limited resources to screen studies; a sensitivity-and-precision-maximising filter with higher sensitivity (81%; 77%-83%) but lower precision (32%; 28%-36%), providing a balance between expediency and comprehensiveness; and a sensitivity-maximising filter with high sensitivity (88%; 85%-90%) but likely very low precision, useful when combined with specific content terms. Similar sensitivity estimates were found for PubMed versions. CONCLUSION: Our filters strike different balances between comprehensiveness and screening workload and suit different research needs. Retrieval of ITS studies would be improved if authors identified the ITS design in the titles.

Efficacy of searching in biomedical databases beyond MEDLINE in identifying randomised controlled trials on hyperbaric oxygen treatment.

Introduction: Literature searches are routinely used by researchers for conducting systematic reviews as well as by healthcare providers, and sometimes patients, to quickly guide their clinical decisions. Using more than one database is generally recommended but may not always be necessary for some fields. This study aimed to determine the added value of searching additional databases beyond MEDLINE when conducting a literature search of hyperbaric oxygen treatment (HBOT) randomised controlled trials (RCTs). Methods: This study consisted of two phases: a scoping review of all RCTs in the field of HBOT, followed by a a statistical analysis of sensitivity, precision, 'number needed to read' (NNR) and 'number unique' included by individual biomedical databases. MEDLINE, Embase, Cochrane Central Register of Control Trials (CENTRAL), and Cumulated Index to Nursing and Allied Health Literature (CINAHL) were searched without date or language restrictions up to December 31, 2022. Screening and data extraction were conducted in duplicate by pairs of independent reviewers. RCTs were included if they involved human subjects and HBOT was offered either on its own or in combination with other treatments. Results: Out of 5,840 different citations identified, 367 were included for analysis. CENTRAL was the most sensitive (87.2%) and had the most unique references (7.1%). MEDLINE had the highest precision (23.8%) and optimal NNR (four). Among included references, 14.2% were unique to a single database. Conclusions: Systematic reviews of RCTs in HBOT should always utilise multiple databases, which at minimum include MEDLINE, Embase, CENTRAL and CINAHL.

Engaging people with lived experiences on community advisory boards in community-based participatory research: a scoping review protocol.

INTRODUCTION: Community-based participatory research (CBPR) is a collaborative research approach that equally engages researchers and community stakeholders throughout all steps of the research process to facilitate social change and increase research relevance. Community advisory boards (CABs) are a CBPR tool in which individuals with lived experience and community organisations are integrated into the research process and ensure the work aligns with community priorities. We seek to (1) explore the best practices for the recruitment and engagement of people with lived experiences on CABs and (2) identify the scope of literature on minimising power dynamics between organisations and community members with lived experience who work on CABs together. METHODS AND ANALYSIS: This scoping review will follow the Arksey and O'Malley methodological framework, informed by Levac et al, and will be reported using a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram. Detailed and robust search strategies have been developed for Embase, Medline and PsychINFO. Grey literature references and reference lists of included articles published between 1 January 1990 and 30 March 2023 will be considered. Two reviewers will independently screen references in two successive stages of title/abstract and full-text screening. Conflicts will be decided by consensus or a third reviewer. Thematic analysis will be applied in three phases: open coding, axial coding and abstraction. Extracted data will be recorded and presented in a tabular format and/or graphical summaries, with a descriptive overview discussing how the research findings relate to the research questions. At this time, a preliminary search of peer-reviewed and grey literature has been conducted. Search results for peer-reviewed literature have been uploaded to Covidence for review and appraisal for relevance. ETHICS AND DISSEMINATION: Formal ethics approval is not required for this review. Review findings will inform ongoing and future CBPR community advisory board dynamics. REGISTRATION: The protocol has been registered prospectively on the Open Science Framework (https://doi.org/10.17605/OSF.IO/QF5D3).

Assessing the research landscape and clinical utility of large language models: a scoping review.

IMPORTANCE: Large language models (LLMs) like OpenAI's ChatGPT are powerful generative systems that rapidly synthesize natural language responses. Research on LLMs has revealed their potential and pitfalls, especially in clinical settings. However, the evolving landscape of LLM research in medicine has left several gaps regarding their evaluation, application, and evidence base. OBJECTIVE: This scoping review aims to (1) summarize current research evidence on the accuracy and efficacy of LLMs in medical applications, (2) discuss the ethical, legal, logistical, and socioeconomic implications of LLM use in clinical settings, (3) explore barriers and facilitators to LLM implementation in healthcare, (4) propose a standardized evaluation framework for assessing LLMs' clinical utility, and (5) identify evidence gaps and propose future research directions for LLMs in clinical applications. EVIDENCE REVIEW: We screened 4,036 records from MEDLINE, EMBASE, CINAHL, medRxiv, bioRxiv, and arXiv from January 2023 (inception of the search) to June 26, 2023 for English-language papers and analyzed findings from 55 worldwide studies. Quality of evidence was reported based on the Oxford Centre for Evidence-based Medicine recommendations. FINDINGS: Our results demonstrate that LLMs show promise in compiling patient notes, assisting patients in navigating the healthcare system, and to some extent, supporting clinical decision-making when combined with human oversight. However, their utilization is limited by biases in training data that may harm patients, the generation of inaccurate but convincing information, and ethical, legal, socioeconomic, and privacy concerns. We also identified a lack of standardized methods for evaluating LLMs' effectiveness and feasibility. CONCLUSIONS AND RELEVANCE: This review thus highlights potential future directions and questions to address these limitations and to further explore LLMs' potential in enhancing healthcare delivery.

Protocol for a scoping review of factors associated with disparities in clinical provision of deep brain stimulation.

INTRODUCTION: Deep brain stimulation (DBS) can be used to treat several neurological and psychiatric conditions such as Parkinson's disease, epilepsy and obsessive-compulsive disorder; however, limited work has been done to assess the disparities in DBS access and implementation. The goal of this scoping review is to identify sources of disparity in the clinical provision of DBS. METHODS AND ANALYSIS: A scoping review will be conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-extension for Scoping Reviews methodology. Relevant studies will be identified from databases including MEDLINE/PubMed, EMBASE and Web of Science, as well as reference lists from retained articles. Initial search dates were in January 2023, with the study still ongoing. An initial screening of the titles and abstracts of potentially eligible studies will be completed, with relevant studies collected for full-text review. The principal investigators and coauthors will then independently review all full-text articles meeting the inclusion criteria. Data will be extracted and collected in table format. Finally, results will be synthesised in a table and narrative report. ETHICS AND DISSEMINATION: No institutional board review or approval is necessary for the proposed scoping review. The findings will be submitted for publication to relevant peer-reviewed journals and conferences. SCOPING REVIEW REGISTRATION: This protocol has been registered prospectively on the Open Science Framework (https://osf.io/cxvhu).

Voice disorder recognition using machine learning: a scoping review protocol.

INTRODUCTION: Over the past decade, several machine learning (ML) algorithms have been investigated to assess their efficacy in detecting voice disorders. Literature indicates that ML algorithms can detect voice disorders with high accuracy. This suggests that ML has the potential to assist clinicians in the analysis and treatment outcome evaluation of voice disorders. However, despite numerous research studies, none of the algorithms have been sufficiently reliable to be used in clinical settings. Through this review, we aim to identify critical issues that have inhibited the use of ML algorithms in clinical settings by identifying standard audio tasks, acoustic features, processing algorithms and environmental factors that affect the efficacy of those algorithms. METHODS: We will search the following databases: Web of Science, Scopus, Compendex, CINAHL, Medline, IEEE Explore and Embase. Our search strategy has been developed with the assistance of the university library staff to accommodate the different syntactical requirements. The literature search will include the period between 2013 and 2023, and will be confined to articles published in English. We will exclude editorials, ongoing studies and working papers. The selection, extraction and analysis of the search data will be conducted using the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews' system. The same system will also be used for the synthesis of the results. ETHICS AND DISSEMINATION: This scoping review does not require ethics approval as the review solely consists of peer-reviewed publications. The findings will be presented in peer-reviewed publications related to voice pathology.

Trust and acceptability of data-driven clinical recommendations in everyday practice: A scoping review.

BACKGROUND: Increasing attention is being given to the analysis of large health datasets to derive new clinical decision support systems (CDSS). However, few data-driven CDSS are being adopted into clinical practice. Trust in these tools is believed to be fundamental for acceptance and uptake but to date little attention has been given to defining or evaluating trust in clinical settings. OBJECTIVES: A scoping review was conducted to explore how and where acceptability and trustworthiness of data-driven CDSS have been assessed from the health professional's perspective. METHODS: Medline, Embase, PsycInfo, Web of Science, Scopus, ACM Digital, IEEE Xplore and Google Scholar were searched in March 2022 using terms expanded from: "data-driven" AND "clinical decision support" AND "acceptability". Included studies focused on healthcare practitioner-facing data-driven CDSS, relating directly to clinical care. They included trust or a proxy as an outcome, or in the discussion. The preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) is followed in the reporting of this review. RESULTS: 3291 papers were screened, with 85 primary research studies eligible for inclusion. Studies covered a diverse range of clinical specialisms and intended contexts, but hypothetical systems (24) outnumbered those in clinical use (18). Twenty-five studies measured trust, via a wide variety of quantitative, qualitative and mixed methods. A further 24 discussed themes of trust without it being explicitly evaluated, and from these, themes of transparency, explainability, and supporting evidence were identified as factors influencing healthcare practitioner trust in data-driven CDSS. CONCLUSION: There is a growing body of research on data-driven CDSS, but few studies have explored stakeholder perceptions in depth, with limited focused research on trustworthiness. Further research on healthcare practitioner acceptance, including requirements for transparency and explainability, should inform clinical implementation.

Alpha-lipoic acid for diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a frequent complication in people living with type 1 or type 2 diabetes. There is currently no effective treatment for DPN. Although alpha-lipoic acid (ALA, also known as thioctic acid) is widely used, there is no consensus about its benefits and harms. OBJECTIVES: To assess the effects of alpha-lipoic acid as a disease-modifying agent in people with diabetic peripheral neuropathy. SEARCH METHODS: On 11 September 2022, we searched the Cochrane Neuromuscular Specialised Register, CENTRAL, MEDLINE, Embase, and two clinical trials registers. We also searched the reference lists of the included studies and relevant review articles for additional references not identified by the electronic searches. SELECTION CRITERIA: We included randomised clinical trials (RCTs) that compared ALA with placebo in adults (aged 18 years or older) and that applied the study interventions for at least six months. There were no language restrictions. DATA COLLECTION AND ANALYSIS: We used standard methods expected by Cochrane. The primary outcome was change in neuropathy symptoms expressed as changes in the Total Symptom Score (TSS) at six months after randomisation. Secondary outcomes were change in neuropathy symptoms at six to 12 months and at 12 to 24 months, change in impairment, change in any validated quality of life total score, complications of DPN, and adverse events. We assessed the certainty of the evidence using GRADE. MAIN RESULTS: Our analysis incorporated three trials involving 816 participants. Two studies included people with type 1 or type 2 diabetes, while one study included only people with type 2 diabetes. The duration of treatment was between six months and 48 months. We judged all studies at high risk of overall bias due to attrition. ALA compared with placebo probably has little or no effect on neuropathy symptoms measured by TSS (lower score is better) after six months (mean difference (MD) -0.16 points, 95% confidence interval (CI) -0.83 to 0.51; 1 study, 330 participants; moderate-certainty evidence). The CI of this effect estimate did not contain the minimal clinically important difference (MCID) of 0.97 points. ALA compared with placebo may have little or no effect on impairment measured by the Neuropathy Impairment Score-Lower Limbs (NIS-LL; lower score is better) after six months (MD -1.02 points, 95% CI -2.93 to 0.89; 1 study, 245 participants; low-certainty evidence). However, we cannot rule out a significant benefit, because the lower limit of the CI surpassed the MCID of 2 points. There is probably little or no difference between ALA and placebo in terms of adverse events leading to cessation of treatment within six months (risk ratio (RR) 1.48, 95% CI 0.50 to 4.35; 3 studies, 1090 participants; moderate-certainty evidence). No studies reported quality of life or complications associated with DPN. AUTHORS' CONCLUSIONS: Our analysis suggests that ALA probably has little or no effect on neuropathy symptoms or adverse events at six months, and may have little or no effect on impairment at six months. All the studies were at high risk of attrition bias. Therefore, future RCTs should ensure complete follow-up and transparent reporting of any participants missing from the analyses.

Methods and measures to evaluate the impact of participatory model building on public policymakers: a scoping review protocol.

INTRODUCTION: Public policymakers are increasingly engaged in participatory model building processes, such as group model building. Understanding the impacts of policymaker participation in these processes on policymakers is important given that their decisions often have significant influence on the dynamics of complex systems that affect health. Little is known about the extent to which the impacts of participatory model building on public policymakers have been evaluated or the methods and measures used to evaluate these impacts. METHODS AND ANALYSIS: A scoping review protocol was developed with the objectives of: (1) scoping studies that have evaluated the impacts of facilitated participatory model building processes on public policymakers who participated in these processes; and (2) describing methods and measures used to evaluate impacts and the main findings of these evaluations. The Joanna Briggs Institute's Population, Concept, Context framework was used to formulate the article identification process. Seven electronic databases-MEDLINE (Ovid), ProQuest Health and Medical, Scopus, Web of Science, Embase (Ovid), CINAHL Complete and PsycInfo-will be searched. Identified articles will be screened according to inclusion and exclusion criteria and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist for scoping reviews will be used and reported. A data extraction tool will collect information across three domains: study characteristics, methods and measures, and findings. The review will be conducted using Covidence, a systematic review data management platform. ETHICS AND DISSEMINATION: The scoping review produced will generate an overview of how public policymaker engagement in participatory model building processes has been evaluated. Findings will be disseminated through peer-reviewed publications and to communities of practice that convene policymakers in participatory model building processes. This review will not require ethics approval because it is not human subject research.

Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review.

BACKGROUND: Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies. This scoping review aims to survey the literature to provide a comprehensive summary of the key factors influencing AI acceptability among healthcare professionals in medical imaging domains and the different approaches which have been taken to investigate them. METHODS: A systematic literature search was performed across five academic databases including Medline, Cochrane Library, Web of Science, Compendex, and Scopus from January 2013 to September 2023. This was done in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Overall, 31 articles were deemed appropriate for inclusion in the scoping review. RESULTS: The literature has converged towards three overarching categories of factors underpinning AI acceptability including: user factors involving trust, system understanding, AI literacy, and technology receptiveness; system usage factors entailing value proposition, self-efficacy, burden, and workflow integration; and socio-organisational-cultural factors encompassing social influence, organisational readiness, ethicality, and perceived threat to professional identity. Yet, numerous studies have overlooked a meaningful subset of these factors that are integral to the use of medical AI systems such as the impact on clinical workflow practices, trust based on perceived risk and safety, and compatibility with the norms of medical professions. This is attributable to reliance on theoretical frameworks or ad-hoc approaches which do not explicitly account for healthcare-specific factors, the novelties of AI as software as a medical device (SaMD), and the nuances of human-AI interaction from the perspective of medical professionals rather than lay consumer or business end users. CONCLUSION: This is the first scoping review to survey the health informatics literature around the key factors influencing the acceptability of AI as a digital healthcare intervention in medical imaging contexts. The factors identified in this review suggest that existing theoretical frameworks used to study AI acceptability need to be modified to better capture the nuances of AI deployment in healthcare contexts where the user is a healthcare professional influenced by expert knowledge and disciplinary norms. Increasing AI acceptability among medical professionals will critically require designing human-centred AI systems which go beyond high algorithmic performance to consider accessibility to users with varying degrees of AI literacy, clinical workflow practices, the institutional and deployment context, and the cultural, ethical, and safety norms of healthcare professions. As investment into AI for healthcare increases, it would be valuable to conduct a systematic review and meta-analysis of the causal contribution of these factors to achieving high levels of AI acceptability among medical professionals.

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.

Generative adversarial networks in dental imaging: a systematic review.

OBJECTIVES: This systematic review on generative adversarial network (GAN) architectures for dental image analysis provides a comprehensive overview to readers regarding current GAN trends in dental imagery and potential future applications. METHODS: Electronic databases (PubMed/MEDLINE, Scopus, Embase, and Cochrane Library) were searched to identify studies involving GANs for dental image analysis. Eighteen full-text articles describing the applications of GANs in dental imagery were reviewed. Risk of bias and applicability concerns were assessed using the QUADAS-2 tool. RESULTS: GANs were used for various imaging modalities, including two-dimensional and three-dimensional images. In dental imaging, GANs were utilized for tasks such as artifact reduction, denoising, and super-resolution, domain transfer, image generation for augmentation, outcome prediction, and identification. The generated images were incorporated into tasks such as landmark detection, object detection and classification. Because of heterogeneity among the studies, a meta-analysis could not be conducted. Most studies (72%) had a low risk of bias in all four domains. However, only three (17%) studies had a low risk of applicability concerns. CONCLUSIONS: This extensive analysis of GANs in dental imaging highlighted their broad application potential within the dental field. Future studies should address limitations related to the stability, repeatability, and overall interpretability of GAN architectures. By overcoming these challenges, the applicability of GANs in dentistry can be enhanced, ultimately benefiting the dental field in its use of GANs and artificial intelligence.