Social desirability bias is related to children's energy intake in a laboratory test meal paradigm.

Dietary intake is notoriously difficult to measure in children. Laboratory test meals address some of the methodological concerns of self-report methods, but may also be susceptible to social desirability bias, referring to the tendency for individuals to adjust their behaviors in order to be perceived more positively. The aim of the current study was to evaluate whether social desirability bias was associated with children's energy intake during a laboratory test meal, and whether this association varied by food type (total caloric intake, snack food intake, fruit/vegetable intake) and sex. A total of 82 children (M age = 9.45 ± 0.85; 50 % girls; 84.1 % rural; 85.4 % White) completed several surveys, including the Children's Social Desirability Scale and had their body composition measured. At lunchtime, they were granted access to a multi-array test meal (>5000 kcal). After adjusting for lean mass, fat mass, depressive symptoms, and parental food restriction, children who reported higher social desirability bias consumed fewer calories from snack foods (B = -11.58, p = .009, semi-partial correlation = -0.28). Boys with higher social desirability bias consumed less calories from fruits and vegetables (B = -6.47, p = .010, semi-partial correlation = -0.411); this association was not significant in girls. The desire to be perceived in a positive manner may influence children's eating behaviors in experimental paradigms. Replication studies with larger, more diverse pediatric samples are needed, as are strategies to reduce the effects of social desirability bias on test meal intake in order to enhance the validity of this dietary assessment approach.

Prognostic uncertainty in multiple sclerosis: A concept analysis.

AIM: To report an analysis of the concept of prognostic uncertainty in patients with multiple sclerosis (MS). BACKGROUND: The complexity and ambiguity involved in a diagnosis of MS lead to the occurrence of prognostic uncertainty among patients. A concept analysis is presented that analyses what prognostic uncertainty means to those experiencing the transition between relapsing-remitting multiple sclerosis and secondary progressive multiple sclerosis. DESIGN: Concept analysis. DATA SOURCES: PubMed, Ovid Medline, Cumulative Index for Nursing and Allied Health Literature databases were searched for literature published within the last 10 years using combinations of the terms prognostic and diagnostic uncertainty, and multiple sclerosis along with archival referencing. METHODS: The Walker and Avant method was used to analyse the concept of prognostic uncertainty in patients with MS. RESULTS: The defining attributes identified that provide additional context to prognostic uncertainty are illness uncertainty, intolerance of uncertainty and progressive dwindling. Related, contrary, model and borderline cases are presented to further discuss the application of the key attributes to the concept. CONCLUSION: There are limited data on prognostic uncertainty and multiple sclerosis; however, patients and physicians express uncertainty in understanding one's disease trajectory and determining when a patient with relapsing-remitting multiple sclerosis has entered the secondary progressive multiple sclerosis disease course leading to ineffective communication and frustration. RELEVANCE TO CLINICAL PRACTICE: Genetics and genomics have the potential to provide a prognostic factor for addressing the concept of uncertainty as it relates to persons with multiple sclerosis. Moving beyond the concept analysis, a case is made for nurse involvement in genetic and genomic research to conduct trials, translate, and apply these findings to clinical practice and nursing curricula, addressing the uncertainty experienced by those afflicted with chronic illnesses, such as multiple sclerosis.

Haemodynamic-guided management of heart failure (GUIDE-HF): a randomised controlled trial.

BACKGROUND: Previous studies have suggested that haemodynamic-guided management using an implantable pulmonary artery pressure monitor reduces heart failure hospitalisations in patients with moderately symptomatic (New York Heart Association [NYHA] functional class III) chronic heart failure and a hospitalisation in the past year, irrespective of ejection fraction. It is unclear if these benefits extend to patients with mild (NYHA functional class II) or severe (NYHA functional class IV) symptoms of heart failure or to patients with elevated natriuretic peptides without a recent heart failure hospitalisation. This trial was designed to evaluate whether haemodynamic-guided management using remote pulmonary artery pressure monitoring could reduce heart failure events and mortality in patients with heart failure across the spectrum of symptom severity (NYHA funational class II-IV), including those with elevated natriuretic peptides but without a recent heart failure hospitalisation. METHODS: The randomised arm of the haemodynamic-GUIDEed management of Heart Failure (GUIDE-HF) trial was a multicentre, single-blind study at 118 centres in the USA and Canada. Following successful implantation of a pulmonary artery pressure monitor, patients with all ejection fractions, NYHA functional class II-IV chronic heart failure, and either a recent heart failure hospitalisation or elevated natriuretic peptides (based on a-priori thresholds) were randomly assigned (1:1) to either haemodynamic-guided heart failure management based on pulmonary artery pressure or a usual care control group. Patients were masked to their study group assignment. Investigators were aware of treatment assignment but did not have access to pulmonary artery pressure data for control patients. The primary endpoint was a composite of all-cause mortality and total heart failure events (heart failure hospitalisations and urgent heart failure hospital visits) at 12 months assessed in all randomly assigned patients. Safety was assessed in all patients. A pre-COVID-19 impact analysis for the primary and secondary outcomes was prespecified. This study is registered with ClinicalTrials.gov, NCT03387813. FINDINGS: Between March 15, 2018, and Dec 20, 2019, 1022 patients were enrolled, with 1000 patients implanted successfully, and follow-up was completed on Jan 8, 2021. There were 253 primary endpoint events (0·563 per patient-year) among 497 patients in the haemodynamic-guided management group (treatment group) and 289 (0·640 per patient-year) in 503 patients in the control group (hazard ratio [HR] 0·88, 95% CI 0·74-1·05; p=0·16). A prespecified COVID-19 sensitivity analysis using a time-dependent variable to compare events before COVID-19 and during the pandemic suggested a treatment interaction (pinteraction=0·11) due to a change in the primary endpoint event rate during the pandemic phase of the trial, warranting a pre-COVID-19 impact analysis. In the pre-COVID-19 impact analysis, there were 177 primary events (0·553 per patient-year) in the intervention group and 224 events (0·682 per patient-year) in the control group (HR 0·81, 95% CI 0·66-1·00; p=0·049). This difference in primary events almost disappeared during COVID-19, with a 21% decrease in the control group (0·536 per patient-year) relative to pre-COVID-19, virtually no change in the treatment group (0·597 per patient-year), and no difference between groups (HR 1·11, 95% CI 0·80-1·55; p=0·53). The cumulative incidence of heart failure events was not reduced by haemodynamic-guided management (0·85, 0·70-1·03; p=0·096) in the overall study analysis but was significantly decreased in the pre-COVID-19 impact analysis (0·76, 0·61-0·95; p=0·014). 1014 (99%) of 1022 patients had freedom from device or system-related complications. INTERPRETATION: Haemodynamic-guided management of heart failure did not result in a lower composite endpoint rate of mortality and total heart failure events compared with the control group in the overall study analysis. However, a pre-COVID-19 impact analysis indicated a possible benefit of haemodynamic-guided management on the primary outcome in the pre-COVID-19 period, primarily driven by a lower heart failure hospitalisation rate compared with the control group. FUNDING: Abbott.

Comprehensiveness, Accuracy, and Readability of Exercise Recommendations Provided by an AI-Based Chatbot: Mixed Methods Study.

BACKGROUND: Regular physical activity is critical for health and disease prevention. Yet, health care providers and patients face barriers to implement evidence-based lifestyle recommendations. The potential to augment care with the increased availability of artificial intelligence (AI) technologies is limitless; however, the suitability of AI-generated exercise recommendations has yet to be explored. OBJECTIVE: The purpose of this study was to assess the comprehensiveness, accuracy, and readability of individualized exercise recommendations generated by a novel AI chatbot. METHODS: A coding scheme was developed to score AI-generated exercise recommendations across ten categories informed by gold-standard exercise recommendations, including (1) health condition-specific benefits of exercise, (2) exercise preparticipation health screening, (3) frequency, (4) intensity, (5) time, (6) type, (7) volume, (8) progression, (9) special considerations, and (10) references to the primary literature. The AI chatbot was prompted to provide individualized exercise recommendations for 26 clinical populations using an open-source application programming interface. Two independent reviewers coded AI-generated content for each category and calculated comprehensiveness (%) and factual accuracy (%) on a scale of 0%-100%. Readability was assessed using the Flesch-Kincaid formula. Qualitative analysis identified and categorized themes from AI-generated output. RESULTS: AI-generated exercise recommendations were 41.2% (107/260) comprehensive and 90.7% (146/161) accurate, with the majority (8/15, 53%) of inaccuracy related to the need for exercise preparticipation medical clearance. Average readability level of AI-generated exercise recommendations was at the college level (mean 13.7, SD 1.7), with an average Flesch reading ease score of 31.1 (SD 7.7). Several recurring themes and observations of AI-generated output included concern for liability and safety, preference for aerobic exercise, and potential bias and direct discrimination against certain age-based populations and individuals with disabilities. CONCLUSIONS: There were notable gaps in the comprehensiveness, accuracy, and readability of AI-generated exercise recommendations. Exercise and health care professionals should be aware of these limitations when using and endorsing AI-based technologies as a tool to support lifestyle change involving exercise.

Comprehensive Cardiovascular Risk Factor Control With a Mobile Health Cardiovascular Risk Self-Management Program.

BACKGROUND: Mobile health technology's impact on cardiovascular risk factor control is not fully understood. This study evaluates the association between interaction with a mobile health application and change in cardiovascular risk factors. METHODS AND RESULTS: Participants with hypertension with or without dyslipidemia enrolled in a workplace-deployed mobile health application-based cardiovascular risk self-management program between January 2018 and December 2022. Retrospective evaluation explored the influence of application engagement on change in blood pressure (BP), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and weight. Multiple regression analyses examined the influence of guideline-based, nonpharmacological lifestyle-based digital coaching on outcomes adjusting for confounders. Of 102 475 participants, 49.1% were women. Median age was 53 (interquartile range, 43-61) years, BP was 134 (interquartile range, 124-144)/84 (interquartile range, 78-91) mm Hg, TC was 183 (interquartile range, 155-212) mg/dL, LDL-C was 106 (82-131) mg/dL, and body mass index was 30 (26-35) kg/m2. At 2 years, participants with baseline systolic BP ≥140 mm Hg reduced systolic BP by 18.6 (SEM, 0.3) mm Hg. At follow up, participants with baseline TC ≥240 mg/dL reduced TC by 65.7 (SEM, 4.6) mg/dL, participants with baseline LDL-C≥160 mg/dL reduced LDL-C by 66.6 (SEM, 6.2) mg/dL, and participants with baseline body mass index ≥30 kg/m2 lost 12.0 (SEM, 0.3) pounds, or 5.1% of body weight. Interaction with digital coaching was associated with greater reduction in all outcomes. CONCLUSIONS: A mobile health application-based cardiovascular risk self-management program was associated with favorable reductions in BP, TC, LDL-C, and weight, highlighting the potential use of this technology in comprehensive cardiovascular risk factor control.

DJ-1 acts in parallel to the PINK1/parkin pathway to control mitochondrial function and autophagy.

Mutations in DJ-1, PINK1 (PTEN-induced putative kinase 1) and parkin all cause recessive parkinsonism in humans, but the relationships between these genes are not clearly defined. One event associated with loss of any of these genes is altered mitochondrial function. Recent evidence suggests that turnover of damaged mitochondria by autophagy might be central to the process of recessive parkinsonism. Here, we show that loss of DJ-1 leads to loss of mitochondrial polarization, fragmentation of mitochondria and accumulation of markers of autophagy (LC3 punctae and lipidation) around mitochondria in human dopaminergic cells. These effects are due to endogenous oxidative stress, as antioxidants will reverse all of them. Similar to PINK1 and parkin, DJ-1 also limits mitochondrial fragmentation in response to the mitochondrial toxin rotenone. Furthermore, overexpressed parkin will protect against loss of DJ-1 and, although DJ-1 does not alter PINK1 mitochondrial phenotypes, DJ-1 is still active against rotenone-induced damage in the absence of PINK1. None of the three proteins complex together using size exclusion chromatography. These data suggest that DJ-1 works in parallel to the PINK1/parkin pathway to maintain mitochondrial function in the presence of an oxidative environment.

A mathematical model of chlamydial infection incorporating movement of chlamydial particles.

We present a spatiotemporal mathematical model of chlamydial infection, host immune response, and movement of infectious particles. The resulting partial differential equations model both the dynamics of the infection and changes in infection profile observed spatially along the length of the host genital tract. This model advances previous Chlamydia modelling by incorporating spatial change. Numerical solutions and model analysis are carried out, and we present a hypothesis regarding the potential for treatment and prevention of infection by increasing chlamydial particle motility.

Randomized Trial of Promoting First Relationships: Effects on Maltreated Toddlers' Separation Distress and Sleep Regulation after Reunification.

This study investigates the impact of an attachment focused intervention, Promoting First Relationships (PFR), on sleep problems among toddlers in child welfare recently reunified with their birth parent. Recently reunified parent-toddler dyads (n = 43) were drawn from a larger random control trial. Toddlers (11-36 months) and their parents were assessed in two-hour research home visits at enrollment (baseline), and a 6-month post-intervention follow-up. Measures included parental report of sleep problems and research visitor observation of separation distress (using the Toddler Attachment Sort-45). The PFR intervention predicted fewer sleep problems, adjusting for a baseline measure of sleep problems and other covariates. A path model showed evidence of an indirect effect of PFR on sleep problems through declines in separation distress. An attachment focused intervention like PFR that reduces infant separation distress can lead to reductions in sleep problems.

Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals.

PURPOSE: The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals. METHOD: In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team. RESULTS: Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care. CONCLUSIONS: The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.

Clinical use of artificial intelligence requires AI-capable organizations.

Artificial intelligence-based algorithms are being widely implemented in health care, even as evidence is emerging of bias in their design, problems with implementation, and potential harm to patients. To achieve the promise of using of AI-based tools to improve health, healthcare organizations will need to be AI-capable, with internal and external systems functioning in tandem to ensure the safe, ethical, and effective use of AI-based tools. Ideas are starting to emerge about the organizational routines, competencies, resources, and infrastructures that will be required for safe and effective deployment of AI in health care, but there has been little empirical research. Infrastructures that provide legal and regulatory guidance for managers, clinician competencies for the safe and effective use of AI-based tools, and learner-centric resources such as clear AI documentation and local health ecosystem impact reviews can help drive continuous improvement.

The development and implementation of a new hospital performance measure to assess hospital contributions to community health and equity.

OBJECTIVE: To develop and implement a measure of how US hospitals contribute to community health with a focus on equity. DATA SOURCES: Primary data from public comments and hospital surveys and secondary data from the IBM Watson Top 100 Hospitals program collected in the United States in 2020 and 2021. STUDY DESIGN: A thematic analysis of public comments on the proposed measure was conducted using an iterative grounded approach for theme identification. A cross-sectional survey of 207 hospitals was conducted to assess self-attestation to 28 community health best practice standards in the revised measure. An analysis of hospital rankings before and after inclusion of the new measure was performed. DATA COLLECTION/EXTRACTION METHODS: Public comment on the proposed measure was collected via an online survey, email, and virtual meetings in 2020. The survey of hospitals was conducted online by IBM in 2021. The analysis of hospital ranking compared the 2020 and 2021 IBM Watson Top 100 Hospitals program results. PRINCIPAL FINDINGS: More than 650 discrete comments from 83 stakeholders were received and analyzed during measure development. Key themes identified in thematic analysis included equity, fairness, and community priorities. Hospitals that responded to a cross-sectional survey reported meeting on average 76% of applicable best practice standards. Least met standards included providing emergent buprenorphine treatment for opioid use disorder (53%), supporting an evidence-based home visiting program (53%), and establishing a returning citizens employment program (27%). Thirty-seven hospitals shifted position in the 100 Top Hospital rankings after the inclusion of the new measure. CONCLUSIONS: There is broad interest in measuring hospital contributions to community health with a focus on equity. Many highly ranked hospitals report meeting best practice standards, but significant gaps remain. Improving measurement to incentivize greater hospital contributions to community health and equity is an important priority.

Digital Health Interventions to Enhance Prevention in Primary Care: Scoping Review.

BACKGROUND: Disease prevention is a central aspect of primary care practice and is comprised of primary (eg, vaccinations), secondary (eg, screenings), tertiary (eg, chronic condition monitoring), and quaternary (eg, prevention of overmedicalization) levels. Despite rapid digital transformation of primary care practices, digital health interventions (DHIs) in preventive care have yet to be systematically evaluated. OBJECTIVE: This review aimed to identify and describe the scope and use of current DHIs for preventive care in primary care settings. METHODS: A scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and Medical Subject Headings terms covering primary care professionals, prevention and care management, and digital health. A subgroup analysis identified relevant studies conducted in US primary care settings, excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (eg, health care performance and implementation science), and study quality as per Oxford levels of evidence were abstracted. RESULTS: The search yielded 5274 citations, of which 1060 full-text articles were identified. Following a subgroup analysis, 241 articles met the inclusion criteria. Studies primarily examined DHIs among health information technologies, including EHRs (166/241, 68.9%), clinical decision support (88/241, 36.5%), telehealth (88/241, 36.5%), and multiple technologies (154/241, 63.9%). DHIs were predominantly used for tertiary prevention (131/241, 54.4%). Of the core primary care functions, comprehensiveness was addressed most frequently (213/241, 88.4%). DHI users were providers (205/241, 85.1%), patients (111/241, 46.1%), or multiple types (89/241, 36.9%). Reported outcomes were primarily clinical (179/241, 70.1%), and statistically significant improvements were common (192/241, 79.7%). Results were summarized across the following 5 topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. The quality of the included studies was moderate to low. CONCLUSIONS: Preventive DHIs in primary care settings demonstrated meaningful improvements in both clinical and nonclinical outcomes, and across user types; however, adoption and implementation in the US were limited primarily to EHR platforms, and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluations of negative results, effects on health disparities, and many other gaps remain to be explored.

Considering Clinician Competencies for the Implementation of Artificial Intelligence-Based Tools in Health Care: Findings From a Scoping Review.

BACKGROUND: The use of artificial intelligence (AI)-based tools in the care of individual patients and patient populations is rapidly expanding. OBJECTIVE: The aim of this paper is to systematically identify research on provider competencies needed for the use of AI in clinical settings. METHODS: A scoping review was conducted to identify articles published between January 1, 2009, and May 1, 2020, from MEDLINE, CINAHL, and the Cochrane Library databases, using search queries for terms related to health care professionals (eg, medical, nursing, and pharmacy) and their professional development in all phases of clinical education, AI-based tools in all settings of clinical practice, and professional education domains of competencies and performance. Limits were provided for English language, studies on humans with abstracts, and settings in the United States. RESULTS: The searches identified 3476 records, of which 4 met the inclusion criteria. These studies described the use of AI in clinical practice and measured at least one aspect of clinician competence. While many studies measured the performance of the AI-based tool, only 4 measured clinician performance in terms of the knowledge, skills, or attitudes needed to understand and effectively use the new tools being tested. These 4 articles primarily focused on the ability of AI to enhance patient care and clinical decision-making by improving information flow and display, specifically for physicians. CONCLUSIONS: While many research studies were identified that investigate the potential effectiveness of using AI technologies in health care, very few address specific competencies that are needed by clinicians to use them effectively. This highlights a critical gap.

The Role of Targeted Osmotic Lysis in the Treatment of Advanced Carcinoma in Companion Animals: A Case Series.

Background: Targeted osmotic lysis (TOL) is a novel technology that involves concomitant stimulation of voltage-gated sodium channels (VGSCs) and the pharmacological blockade of Na+, K+-ATPase causing lysis of highly malignant cancer cells. Hypothesis/Objectives. TOL offers an option for treating advanced carcinomas in companion animals. Animals. Two cats and 2 dogs that presented to veterinary hospitals for evaluation and treatment of one of several forms of carcinoma. Methods: Digoxin was administered to achieve steady-state, therapeutic concentrations. The animals were then exposed to pulsed electric field stimulation. Pre- and posttreatment assessments of tumor size and quality of life were compared. The treatment frequency and survivability varied, based on the patient's premorbid functioning and response to treatment. Results: Regardless of cancer type, TOL consistently increased survival beyond expected, often improving, but without compromising of quality of life. Conclusions and Clinical Importance. TOL warrants consideration as an option for managing advanced carcinomas.

Effectiveness of Contact Tracing for Viral Disease Mitigation and Suppression: Evidence-Based Review.

BACKGROUND: Contact tracing in association with quarantine and isolation is an important public health tool to control outbreaks of infectious diseases. This strategy has been widely implemented during the current COVID-19 pandemic. The effectiveness of this nonpharmaceutical intervention is largely dependent on social interactions within the population and its combination with other interventions. Given the high transmissibility of SARS-CoV-2, short serial intervals, and asymptomatic transmission patterns, the effectiveness of contact tracing for this novel viral agent is largely unknown. OBJECTIVE: This study aims to identify and synthesize evidence regarding the effectiveness of contact tracing on infectious viral disease outcomes based on prior scientific literature. METHODS: An evidence-based review was conducted to identify studies from the PubMed database, including preprint medRxiv server content, related to the effectiveness of contact tracing in viral outbreaks. The search dates were from database inception to July 24, 2020. Outcomes of interest included measures of incidence, transmission, hospitalization, and mortality. RESULTS: Out of 159 unique records retrieved, 45 (28.3%) records were reviewed at the full-text level, and 24 (15.1%) records met all inclusion criteria. The studies included utilized mathematical modeling (n=14), observational (n=8), and systematic review (n=2) approaches. Only 2 studies considered digital contact tracing. Contact tracing was mostly evaluated in combination with other nonpharmaceutical interventions and/or pharmaceutical interventions. Although some degree of effectiveness in decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality was observed, these results were highly dependent on epidemic severity (R0 value), number of contacts traced (including presymptomatic and asymptomatic cases), timeliness, duration, and compliance with combined interventions (eg, isolation, quarantine, and treatment). Contact tracing effectiveness was particularly limited by logistical challenges associated with increased outbreak size and speed of infection spread. CONCLUSIONS: Timely deployment of contact tracing strategically layered with other nonpharmaceutical interventions could be an effective public health tool for mitigating and suppressing infectious outbreaks by decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality.

The potential of artificial intelligence to improve patient safety: a scoping review.

Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors.

Emergency Use of Targeted Osmotic Lysis for the Treatment of a Patient with Aggressive Late-Stage Squamous Cell Carcinoma of the Cervix.

Upregulation of voltage-gated sodium channels (VGSCs) and Na+/K+-ATPase (sodium pumps) is common across most malignant carcinomas. Targeted osmotic lysis (TOL) is a developing technology in which the concomitant stimulation of VGSCs and pharmacological blockade of sodium pumps causes rapid selective osmotic lysis of carcinoma cells. This treatment of cervical carcinoma is evidence that TOL is a safe, well-tolerated and effective treatment for aggressive advanced carcinomas that has the potential to extend life without compromising its quality. TOL is likely to have broad application for the treatment of advanced-stage carcinomas.

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Sex workers can be screened too often: a cost-effectiveness analysis in Victoria, Australia.

OBJECTIVES: Commercial sex is licensed in Victoria, Australia such that sex workers are required to have regular tests for sexually transmitted infections (STIs). However, the incidence and prevalence of STIs in sex workers are very low, especially since there is almost universal condom use at work. We aimed to conduct a cost-effectiveness analysis of the financial cost of the testing policy versus the health benefits of averting the transmission of HIV, syphilis, chlamydia and gonorrhoea to clients. METHODS: We developed a simple mathematical transmission model, informed by conservative parameter estimates from all available data, linked to a cost-effectiveness analysis. RESULTS: We estimated that under current testing rates, it costs over $A90,000 in screening costs for every chlamydia infection averted (and $A600,000 in screening costs for each quality-adjusted life year (QALY) saved) and over $A4,000,000 for every HIV infection averted ($A10,000,000 in screening costs for each QALY saved). At an assumed willingness to pay of $A50,000 per QALY gained, HIV testing should not be conducted less than approximately every 40 weeks and chlamydia testing approximately once per year; in comparison, current requirements are testing every 12 weeks for HIV and every 4 weeks for chlamydia. CONCLUSIONS: Mandatory screening of female sex workers at current testing frequencies is not cost-effective for the prevention of disease in their male clients. The current testing rate required of sex workers in Victoria is excessive. Screening intervals for sex workers should be based on local STI epidemiology and not locked by legislation.

Serosorting may increase the risk of HIV acquisition among men who have sex with men.

BACKGROUND: Serosorting, the practice of seeking to engage in unprotected anal intercourse with partners of the same HIV status as oneself, has been increasing among men who have sex with men. However, the effectiveness of serosorting as a strategy to reduce HIV risk is unclear, especially since it depends on the frequency of HIV testing. METHODS: We estimated the relative risk of HIV acquisition associated with serosorting compared with not serosorting by using a mathematical model, informed by detailed behavioral data from a highly studied cohort of gay men. RESULTS: We demonstrate that serosorting is unlikely to be highly beneficial in many populations of men who have sex with men, especially where the prevalence of undiagnosed HIV infections is relatively high. We find that serosorting is only beneficial in reducing the relative risk of HIV transmission if the prevalence of undiagnosed HIV infections is less than approximately 20% and approximately 40%, in populations of high (70%) and low (20%) treatment rates, respectively, even though treatment reduces the absolute risk of HIV transmission. Serosorting can be expected to lead to increased risk of HIV acquisition in many settings. In settings with low HIV testing rates serosorting can more than double the risk of HIV acquisition. CONCLUSIONS: Therefore caution should be taken before endorsing the practice of serosorting. It is very important to continue promotion of frequent HIV testing and condom use, particularly among people at high risk.