Value in Health

Background High-risk preoperative triage remains fragmented: existing tools often estimate risk without identifying modifiable mechanisms or linking classification to postoperative monitoring, destination planning, and rescue resources. This protocol describes implementation and evaluation of a Reserve-Stress-Rescue (RSR Framework), pathway that operationalizes perioperative high risk as a mismatch among patient physiologic reserve, procedural stress, and system rescue capacity. Approach RSR is a proposed clinician-facing, modular scoring framework for adults undergoing major surgery, especially patients with frailty, multimorbidity, poor functional capacity, anemia or malnutrition, cardiopulmonary disease, or limited postoperative support. Each domain, Reserve, Stress, and Rescue, is scored from 0 to 4 and recorded as both a three-part profile and a total score from 0 to 12. Scores map to Green, Amber, Red, and Crimson triage bands that trigger escalating actions, including targeted optimization, multidisciplinary review, anesthesia and surgical planning, postoperative destination selection, monitoring intensity, and predefined escalation criteria. Validation Plan The initial phase of this study received an exemption determination from the Yale University Institutional Review Board on June 3, 2026, under IRB Protocol ID 2000042729, with exempt categories 2(ii) and 4(iii), including a waiver of HIPAA authorization for access to and use of protected health information as described in the approved protocol. Evaluation will proceed in stages, assessing feasibility, interrater reliability, completeness, acceptability, discrimination, calibration, and clinical utility. Key outcomes include postoperative complications, unplanned escalation of care, intensive care utilization, failure to rescue, mortality, length of stay, triage burden, low-yield testing cascades, and management-changing pathway activation. Conclusion The RSR pathway reframes high-risk status as a modifiable interaction between vulnerability, operative insult, and rescue capacity rather than a fixed patient label. If feasible and valid, RSR may standardize high-risk identification, align perioperative resources with anticipated physiology, improve communication, and support safer, actionable shared decision-making.

Background. Clostridioides difficile infection (CDI) imposes a burden that extends well beyond the gastrointestinal tract, yet existing outcome measures only partially capture the patient experience. We used frontier large language models (LLMs) on patient and caregiver narratives at scale to describe how burden shifts with disease course. Methods. We analyzed 189 testimonials from the Peggy Lillis Foundation corpus, sorted into four cohorts with recurrence (r) and fulminant (f) severity as axes (rfCDI, fCDI, rCDI, non-rfCDI). Two independent LLMs coded eight thematic domains, four fulminant flags, thirteen emerging semantic fields, the dominant dimension, and narrative arcs. Two clinicians independently coded a subset for inter-rater reliability (PABAK, Gwet's AC1). Results. Treatment trajectory was the dominant theme in recurrent disease, whereas death and near-death dominated non-recurrent fulminant narratives. Psychological burden was near-universal in fulminant disease (98.0% in rfCDI, 97.2% in fCDI). Caregiver and bereavement content concentrated in fCDI (66.7%). Diagnostic failure was frequent across recurrent cohorts (47.6 - 56.1%). Bacteriotherapy tracked recurrence (60.2% rfCDI versus 5.6% fCDI). Financial, mental-health, and caregiver burdens were prominent and are currently unaddressed by guidelines. Human-human reliability was substantial (PABAK 0.79 for semantic fields, 0.76 for domains); arc coding was least reliable. Conclusions. Patient narratives reveal a course-dependent, multidimensional burden in CDI. Concrete gaps exist between what patients prioritize, what guidelines recommend, and what therapy access provides. Frontier-LLM coding, validated against clinicians, offers a reproducible route to translate these priorities into research, care, and policy.

Objectives. To present DrugSet, a validated R Shiny application supporting the construction medicinal products codelists based on the Anatomical Therapeutic Chemical (ATC) system and their mapping to Clinical Practice Research Datalink (CPRD) Aurum prodcodes within a single interactive workflow. Materials and Methods. DrugSet comprises four modules: data preparation, ATC-based hierarchical code selection, string-based CPRD Aurum prodcodes mapping, and codelist export. Validation was conducted against World Health Organization (WHO) ATC reference codelists and manually curated prodcodes mappings across three drug classes: metformin, beta-blocking agents, and topical salicylic acid. Sensitivity, specificity, and Positive Predictive Values (PPV) were calculated for ATC codelist generation. Agreement proportions (overlapping against total identified codes) were calculated for prodcodes mapping. Time needed for codelist construction using DrugSet was recorded and compared to manual approaches. Results. DrugSet ATC codelist generation against WHO manual reference achieved 100% sensitivity, specificity, and PPV across all medicinal products. Prodcodes mapping agreement ranged from 89.2% to 98.3% with discrepancies due to missing data in the prodcodes input vocabulary. DrugSet completed codelist construction in 9 minutes compared to 3 hours and 10 minutes manually, across all medicinal products classes. Discussion. DrugSet provides a unified workflow that runs directly on ATC and source CPRD Aurum vocabulary files. The reduction in codelist construction time and export of the generated codelists supports reproducibility in pharmacoepidemiologic studies where codelist creation can represent a significant proportion of study setup time. Conclusion. DrugSet is an open-source, validated tool that improves accuracy, and efficiency of codelist construction for medicinal products based on ATC codes towards CPRD Aurum prodcodes.

Background. Evidence of the effectiveness and cost-effectiveness of new vaccines that reduce the burden of respiratory syncytial virus (RSV) in older populations is emerging. The reported cost-effectiveness of these vaccination strategies varies substantially across different settings. This study assessed the cost-effectiveness of older adult-targeted RSV vaccination strategies in the Australian context and compared findings with published evaluations. Methods. We developed an individual-based dynamic transmission model of RSV infection, linked to a clinical pathways and cost-effectiveness model. We modelled different adult vaccination strategies for the general population and the Indigenous population, and present incremental cost-effectiveness ratios (ICERs) as cost per quality-adjusted life year gained, from a healthcare system perspective. Deterministic and probabilistic sensitivity analyses explored drivers of cost-effectiveness and sensitivity of findings to uncertainty in parameter estimates. Results. Vaccinating the general population of older adults in Australia was not found to be cost-effective at a dose price of 100 AUD, but was found to be cost-saving for Indigenous adults, given the higher disease burden in this population. Individual drivers of ICERs in our setting were dose price, hospitalisation incidence and mortality, however conclusions about cost-effectiveness were robust to joint parameter uncertainty. Conclusions. The cost-effectiveness of vaccinating adults against RSV depends on many uncertain and context-specific quantities. Strategies that target high risk populations were found to be cost-effective in Australia due to the larger avertable burden.

The rapid expansion of the medical artificial intelligence (AI) literature has outpaced our ability to judge how far published models have progressed towards clinical use. We investigated whether the translational maturity of a study can be estimated automatically from its abstract. Using PubMed, we assembled a corpus of 11,024 candidate articles, reduced it to 1,816 AI-related articles by heuristic filtering, and manually double-annotated a balanced sample of 524 articles across five maturity classes (internal validation, external validation, prospective evaluation, implementation or governance, and not applicable). Abstracts were represented as TF-IDF features and classified using multinomial logistic regression with a Lasso penalty, chosen for interpretability and suitability for a small, imbalanced dataset. On a stratified held-out test set (n = 104), the model achieved 69.2% accuracy, Cohen's kappa of 0.495, macro-F1 of 0.458 and a weighted AUC of 0.820. Performance was strong for the frequent classes but poor for the rare implementation or governance class, which the model failed to recover. A balanced manual verification of 200 large-corpus predictions confirmed this pattern, with per-class precision ranging from 82.5% (internal validation) to 5.0% (implementation or governance). An interpretable, low-resource classifier can support literature mapping but requires human oversight for advanced maturity levels.

Meta-analysis usually reduces each study to an effect estimate with a standard error and pools these by inverse-variance weighting: fixed effect (FE), random effects (RE), or unrestricted weighted least squares (UWLS). We propose information-geometric meta-integration (IGMI), representing each study by its sampling distribution, the Gaussian N(theta_i, Sigma_i), and pooling studies as a weighted Frechet mean (barycenter) under Bures-Wasserstein (BW), Fisher-Rao, or Wasserstein-Fisher-Rao (WFR) geometry. In the scalar fixed-variance case the BW barycenter mean is exactly the FE estimate; the minimized Frechet functional reproduces the Higgins-Thompson I^2 and DerSimonian-Laird tau^2 heterogeneity statistics; and a Frechet-scatter pivot reproduces the Hartung-Knapp-Sidik-Jonkman interval at m = 1 and yields an exact Hotelling F(m, K-m) region for m outcomes under proportional total covariances. WFR adds a robust outlier-resistant pool: as its length scale delta grows without bound it converges monotonically to BW, whereas finite delta gives a redescending M-estimator with rejection point exactly pi*delta. Simulations show calibrated multivariate coverage at small K, where Wald intervals undercover, and strong resistance of the equal-weight WFR pool to contamination. In 2,445 Cochrane meta-analyses, WFR most often wins leave-one-out predictive scoring. In 835 bivariate meta-analyses, the closed-form BW barycenter matches REML multivariate meta-analysis predictively and is exactly invariant to the unreported within-study correlation, unlike the likelihood estimate.

Introduction: For oncology patients with limited treatment options, clinical trials may be a critical lifesaving pathway. Identifying relevant trials, however, is a time-consuming and difficult task. Several patient-trial matching processes incorporating large language models (LLMs) have been proposed to alleviate the burden on patients and oncologists. We aim to explore the benefits and practical challenges of zero-shot LLM-assisted trial matching processes by analyzing the results for a single pancreatic cancer patient. Materials and Methods: The results of a simple zero-shot LLM-assisted clinical trial matching process for our patient were compared to those of a "human benchmark," which was developed manually by two of the authors interfacing directly with ClinicalTrials.gov. Performance metrics -- sensitivity, specificity, precision, and accuracy -- were calculated. In addition, a qualitative content analysis (QCA) of LLM reasoning text was done to identify patterns in "errors," which we define as a human-LLM discrepancy in final patient eligibility. Implications and severity of errors are discussed. Results: The zero-shot LLM-assisted process returned potential trials with a sensitivity, specificity, and precision of 81.1%, 89.3%, and 86.5% respectively compared to the human benchmark. Qualitative error analyses revealed that about 73% of errors could potentially be alleviated with improved prompting and information access. Overall performance seemed comparable to that of human reviewers. Conclusion: The results from this preliminary real-world case study provide additional evidence to the literature in support of the integration of LLMs in clinical trial matching to provide benefit to patients with metastatic cancer with limited options.

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been linked to heterogeneous, potentially pleiotropic effects across organ systems, motivating outcome-wide comparative risk profiling in real-world data. A central challenge in such analyses is \emph{residual bias} that remains after adjustment for observed confounders, which can distort effect estimates and mis-calibrate uncertainty. We present distributional diagnosis and calibration (DC), which uses panels of negative control outcomes (NCOs) to diagnose residual bias and calibrate uncertainty. DC evaluates null behavior via $p$-value uniformity and empirical coverage across NCOs, and uses the empirical distribution of NCO effect estimates to calibrate confidence intervals for prespecified primary outcomes. DC is modular: it can wrap around commonly used causal inference methods and operates directly on summary statistics, supporting collaborative research under data-sharing constraints. Using electronic health records from a large U.S. clinical research network (152.7 million patients), we compared GLP-1RAs with sodium--glucose cotransporter~2 inhibitors across 15 prespecified outcomes spanning cardiovascular, mental health, and genitourinary domains using four causal estimators. Across outcomes and methods, DC diagnostics revealed substantial and method-dependent residual systematic error. DC calibration attenuated systematic error signals observed in negative controls and yielded more stable, better-calibrated estimates for clinical outcomes, supporting DC as a practical strategy to strengthen the credibility of real-world comparative effectiveness research.

Abstract Objective To evaluate the adherence to guidelines and readability of large language model-generated sexual health information related to female sexual dysfunction following cystectomy, and to determine whether adherence differs across models and prompt formats. A secondary objective was to introduce an analytic strategy using principal component analysis to examine the dimensions of readability metrics. Methods Three large language models (LLMs), ChatGPT, Gemini, and Perplexity were prompted with six clinical questions related to sexual function after cystectomy. Questions were phrased in long-form and short-form language. Responses were independently graded by two reviewers, derived from guideline recommendations. Linear mixed-effects models predicted adherence as functions of LLM, prompt, and reviewer, with clinical questions as a random intercept. Readability was assessed using five metrics, and principal component analysis (PCA) was used to determine latent structure. Results ChatGPT demonstrated the highest (estimated marginal mean [emm] = 0.769), outperforming Gemini (0.499) and Perplexity (0.457). Shorter, less complex prompts elicited higher adherence than more complex, clinical prompts. All models produced content that exceeded recommended reading levels. PCA demonstrated that a single dominant component accounted for 76.7% of variance across readability indices, indicating a shared underlying construct. Conclusion ChatGPT produced the most guideline-concordant information overall. High linguistic complexity was seen across models, highlighting a barrier to patient comprehension. These findings characterize large language models as variable medical information systems whose outputs rely heavily on prompt structure and model type.

Abstract Climate change is altering environmental conditions that influence foodborne disease transmission, yet traditional systematic reviews cannot keep pace with expanding evidence. We assessed whether an LLM-assisted workflow could generate a rapid, repeatable, and policy-relevant living evidence base for climate-sensitive foodborne disease. We combined structured PubMed searches (2010-2023), gold-standard human labelling, and iterative refinement of a GPT?4?Turbo?based auto-labeller within the SysRev platform. Pathogens of public-health importance in England were selected a priori. Model performance was evaluated against human reviewers using recall, precision, specificity, accuracy, and balanced accuracy. The refined inclusion model achieved 89{middle dot}2% recall, 59{middle dot}2% precision, 84{middle dot}5% specificity, and 85{middle dot}4% accuracy across 1,044 screened abstracts, identifying 436 studies for inclusion. Post-hoc re-evaluation of discordant abstracts showed that records excluded by the model but included during initial human screening did not meet the refined inclusion criteria. Frequently identified climate exposures included rainfall, temperature, seasonality, and humidity; norovirus, Salmonella, Campylobacter, and Cryptosporidium were the most common pathogens. An LLM-assisted workflow can generate living evidence for climate-sensitive foodborne disease with high recall and improved screening consistency. The approach is scalable, auditable, and suitable for secure institutional environments, supporting horizon scanning and climate-health risk assessment.

Cervical cancer is a leading cause of cancer-related mortality among women in Uganda, with women living with HIV (WLHIV) at disproportionate risk due to immunosuppression-driven persistence of human papillomavirus (HPV). Despite national guidelines recommending HPV testing as the preferred screening modality, resource constraints drive continued reliance on visual inspection with acetic acid (VIA), and locally generated cost-effectiveness evidence for WLHIV is limited. This study evaluated the cost-effectiveness of HPV-based versus VIA-based cervical cancer screening among WLHIV in Masaka District, Uganda. A provider-perspective cost-effectiveness analysis was conducted using data from January to December 2024. Costs were estimated using an ingredient-based micro-costing approach capturing personnel, consumables, equipment, and overheads. A total of 1,732 WLHIV aged 25-65 years attended Uganda Cares Masaka: 1,404 screened by HPV-based testing and 326 by VIA. A decision-tree model simulated screening pathways, costs, and outcomes. The primary effectiveness measure was the number of positive cases detected and treated; the incremental cost-effectiveness ratio (ICER) was the primary economic outcome. Deterministic one-way and probabilistic (1,000 Monte Carlo iterations) sensitivity analyses were conducted. HPV-based screening detected 448 positives from 1,404 women screened (31.9%) versus 54 from 326 (16.6%) under VIA. The cost per woman screened was USD 2.58 (HPV) and USD 1.78 (VIA). The ICER was USD 2,895 per additional positive case detected, within the range of ICER estimates reported for cost-effective cervical cancer screening in comparable low- and middle-income settings. The ICER was most sensitive to HPV test kit costs and VIA overhead costs. All 1,000 probabilistic simulations remained in the northeast quadrant of the cost-effectiveness plane, confirming robustness. HPV-based screening is more effective and cost-effective than VIA for cervical cancer screening among WLHIV in Masaka District. These findings support national scale-up of HPV testing within integrated HIV care settings, contingent on procurement efficiencies and strengthened laboratory infrastructure.

Background: Documentation burden significantly impacts nursing workload and well-being, with nurses spending an estimated 20-40% of their time on documentation. Ambient AI technologies offer potential to reduce documentation time by mapping real-time nurse-patient conversations to structured EHR data entries with human-in-the-loop verification. Methods: This protocol describes a pragmatic, EHR-embedded randomized controlled trial evaluating the effectiveness of an Ambient AI tool in reducing nursing documentation time across three inpatient medical/surgical units. The study employs a closed-cohort, stepped-wedge, unit-randomized design, integrating the intervention into routine clinical workflows. The primary outcome is documentation time per shift hour, derived from EHR audit logs. Secondary outcomes include documentation burden, professional well-being, and perceived usability. Results: The trial is being implemented within a shared governance model that integrates executive oversight, operational feasibility, and research rigor. Multidisciplinary workgroups coordinate technical integration, user experience, and analytics, ensuring alignment between operational priorities and pragmatic trial objectives. Early implementation has highlighted the importance of adapting training and analytic strategies to address differential intervention exposure, as well as the need for rapid operational responses to late-emerging technical issues. Discussion: This protocol demonstrates the feasibility of embedding a randomized pragmatic trial within a health system-led operational deployment of Ambient AI for inpatient nursing documentation. The approach highlights the necessity of adapting existing outpatient provider-focused AI implementation strategies for inpatient nursing, emphasizing the unique nature of different nursing care environments. Recruitment challenges and the integration of research with operational workflows are discussed as key considerations for future pragmatic AI trials in nursing. Keywords: Artificial Intelligence; Ambient AI; Nursing Documentation; Documentation Burden; Large Language Models; Speech Recognition Software; Stepped-Wedge Design ClinicalTrials.gov Identifier NCT07456241V4: 2026-05-27 https://clinicaltrials.gov/study/NCT07456241

Background: Hemorrhoids are among the most common anorectal disorders, yet traditional treatment practices remain widespread in Ethiopia. These remedies often involve corrosive chemicals, herbal preparations, or invasive procedures, and are associated with severe complications. Despite their prevalence, systematic evidence on outcomes and community perceptions is limited. Methods: A hospital?based cross?sectional study was conducted from December 30, 2024 to December 29, 2025 in Sidama, Ethiopia. A total of 450 patients diagnosed with hemorrhoids and managed across five government hospitals were enrolled. Structured questionnaires and medical record review were used to collect socio?demographic characteristics, clinical presentation, hospital management, traditional treatment practices, complications, and community perceptions. Descriptive statistics and independent sample t?tests were applied. Results: The mean age of participants was 35.2 years, with a predominance of males (63.1%) and urban residents (72%). Perianal pain (84%) and rectal bleeding (50%) were the most frequent symptoms. Independent samples t?test analysis demonstrated that patients who visited traditional healers were significantly older than those who did not (mean age 48.2 vs. 34.4 years; mean difference = 13.8 years, 95% CI: 8.8-18.8; p < 0.001). Hospital management, primarily hemorrhoidectomy (31.8%), achieved favorable outcomes, with 97.3% of patients improving. Twenty-eight patients (6.2%) reported using traditional healers, most commonly involving topical chemical applications (71.4%). Complications were frequent among traditional users, with 85.7% experiencing adverse outcomes such as persistent pain, anal stenosis, and perianal discharge. Despite these complications, community perceptions remained largely positive or neutral, influenced by family and peers. Conclusion: Traditional hemorrhoid treatment in Southern Ethiopia is associated with high complication rates, yet community perceptions remain favorable due to sociocultural influences. Hospital management demonstrates superior outcomes. Bridging the gap between biomedical care and community beliefs is essential to reduce morbidity and promote safe treatment .

Systematic reviews in computational biology require screening large heterogeneous bibliographic sets, especially when topics span computational methods, cancer genomics and statistical modelling. This paper presents a reproducible semantic triage pipeline that combines SPECTER scientific-document embeddings, research-question similarity, proposal-summary similarity and domain keyword coverage to rank candidate studies for systematic review screening. The pipeline was evaluated on 2,231 Covidence records, including 120 final included studies (prevalence = 5.38%), against keyword-only, TF-IDF, BM25, MiniLM, PubMedBERT and SPECTER-only baselines. SPECTER-hybrid achieved the highest average precision (AP = 0.546), recovered 50% of included studies after screening 4.48% of records, and produced an 11.16-fold enrichment over prevalence. Ablation analysis showed that semantic-keyword combinations consistently outperformed single-signal variants. These findings suggest that citation-informed hybrid ranking can support literature triage while retaining human reviewers as final decision-makers.

Importance: Adverse drug events in older adults are a substantial public health burden, yet spontaneous reporting systems detect them poorly owing to underreporting and the lack of a defined population. These limitations are of particular concern for older adults, who are underrepresented in pre-approval trials yet at elevated risk owing to polypharmacy, multimorbidity, and age-related changes in drug metabolism. Objective: To develop and apply an active, claims-based pharmacovigilance framework using sequential target trial emulation to detect adverse drug event signals in older adults, with atorvastatin as the initial application. Methods: Using Medicare fee-for-service claims (2017-2019), we studied statin-naive beneficiaries aged 65 years or older following myocardial or cerebral infarction. We emulated up to 14 daily sequential trials from the discharge date, classifying patients as initiating atorvastatin (A1), initiating a different medication (A2), or no new medication (A0); the primary contrast was A1 versus A2. For each trial, incident outcomes were ascertained and classified into 552 outcomes based on the Clinical Classifications Software Refined categories. Per-protocol effects were estimated over a 6-month follow-up period using Fine-Gray regression models weighted by the inverse probability of treatment and censoring, treating death as a competing risk, with the false discovery rate controlled via the Benjamini-Hochberg procedure. A signal was declared when the q-value was 0.10 or lower and the subdistribution hazard ratio (sHR) was 1.20 or greater in any prespecified analytic stratum (sensitivity analyses used thresholds of q 0.20 or lower and sHR 1.20 or greater). Results: Of 70,130 eligible patients, 39,948 initiated atorvastatin (A1) and 19,182 initiated another new medication (A2); after weighting, baseline characteristics were closely balanced. After excluding outcomes with sparse cell counts, 295 outcomes were analyzed; five met the primary signal detection criteria: valve disorders (sHR 1.71, 1.20 to 2.43); sprains and strains (sHR 1.79, 1.26 to 2.54); general sensation/perception symptoms (sHR 1.23, 95 percent CI 1.11 to 1.36); abnormal findings without diagnosis (sHR 1.55, 1.18 to 2.05); and prediabetes (sHR 1.71, 1.24 to 2.36). In the sensitivity analysis, we additionally detected posthemorrhagic anemia, hemorrhagic stroke, varicose veins, and other circulatory and skin conditions. Conclusions: An active, claims-based framework using sequential target trial emulation detected both expected and previously unrecognized adverse drug event signals following atorvastatin initiation in older adults, offering a systematic alternative to passive surveillance that can be extended to other commonly prescribed medications.

Background: Within the VEBIS-EHR project, monthly vaccine effectiveness (VE) of COVID-19 vaccines is routinely estimated across EU/EEA countries. While VE quantifies direct protection, it does not capture the overall population benefit of vaccination campaigns in terms of severe outcomes prevented. Aim: To estimate the impact of the 2023 and 2024 autumn COVID-19 vaccination campaigns in adults aged [&ge;]65 years. Methods: We conducted a retrospective cohort study using electronic health records data from Belgium, Denmark, Italy, Navarre (Spain), Portugal, Norway and Sweden. Weekly numbers of averted COVID-19-related hospitalisations and deaths during the 12 months following each campaign were estimated using observed COVID-19-related events, vaccine coverage (VC) and interpolated weekly VE. Results: Across participating countries/regions, among adults aged [&ge;]65 years, the 2023 autumn vaccination campaign averted approximately 6,200 hospitalisations (prevented fraction [PF] 10%) compared with 2,200 (PF 12%) in 2024. Among those aged [&ge;]80 years, the number of averted COVID-19-related deaths was 811 (PF 13%) for the 2023 campaign and 156 (PF 12%) for the 2024 campaign. Impact varied across countries, reflecting differences in VC, vaccination timing and outcome occurrence. Conclusion: The 2023 and 2024 autumn vaccination campaigns resulted in substantially different numbers of averted COVID-19-related hospitalisations and deaths among older adults, with fewer events averted in 2024. These findings highlight that the impact of vaccination programmes depends not only on VC and VE but also on alignment between vaccination timing and periods of increased viral circulation.

Universities provide a key opportunity to deliver social prescribing, a care pathway that aims to connect people with non-medical forms of support within the community to address their social, emotional, and practical needs. However, it is unclear whether students in the UK are aware of social prescribing and whether it would be an acceptable form of support. We surveyed 775 university students across the UK who completed a questionnaire measuring awareness and perceptions of social prescribing. We described awareness and attitudes and used logistic regression to explore how they differed according to individual characteristics. We found an awareness-attitude paradox. Only 25% of students were aware of social prescribing, but attitudes were overwhelmingly positive once explained: 97% thought it could support mental health and wellbeing; 95% believed universities should offer it; and 89% would accept social prescribing if offered by a healthcare professional. Students who were older, postgraduates, and had English as their first language were among those with higher odds of being aware of social prescribing, but positive attitudes were more evenly reported across the sample. Our findings indicate that implementation efforts should prioritise awareness-raising and clear referral pathways, rather than increasing students' willingness to engage with social prescribing.

Introduction: Primary care clinicians (PCC) commonly make decisions in the context of time delay and uncertainty. Delay discounting (DD) and probability discounting (PD) are cognitive biases related to delay and uncertainty that are minimally explored in PCC. We assessed DD and PD in PCC and evaluated their association with low-value care (LVC) decision-making. Methods: We administered a survey to PCC in a Southeastern U.S health system and within the American Academy of Family Physicians networks. The survey comprised standardized psychometric assessments of DD and PD and four LVC clinical vignettes. Outcomes included DD and PD discounting rates for two monetary rewards ($100 and $10,000) and ratings of LVC likelihood (0-100). We used regression analysis with model selection to evaluate the relationship between variables. Results: 225 PCC (89% physicians, 11% advanced practice providers) participated. Heterogeneity in DD and PD rates was observed. For the $10,000 reward, ln k(DD)= -6.80, IQR:-7.60--6.10) and ln h(PD)= 1.75, IQR:1.75-2.36). The reward amount impacted DD and PD in opposing directions (i.e., lower DD/higher PD rates for $10,000 vs. $100). LVC likelihood was highest for low-value antibiotics and lowest for low-value cervical cancer screening (median 20, IQR:10-40 and 0, IQR:0-10, respectively). Model selection revealed demographic associations with LVC likelihood, but no association with DD or PD. Conclusions: Consistent with effects previously reported in non-clinicians, PCC exhibited a range of DD and PD, which ranged by reward magnitude. Neither DD nor PD predicted vignette-based LVC likelihood. Further research should investigate actual clinical practice patterns and other LVC scenarios.

Recently, Hu et al. (2024) conducted a benchmarking study showing that most existing Mendelian randomization (MR) methods exhibit substantial bias and inflated type-I error rates in real data. They attributed these failures to two largely neglected sources of bias: winner's curse and polygenicity-induced bias. Although a few methods have been developed to address one or both of these issues, existing approaches either do not fully account for both biases or are restricted to the univariable setting. In this paper, we propose a multivariable Rao-Blackwellization that corrects winner's curse while accounting for polygenicity and sample structure in a unified framework. Unlike univariable Rao-Blackwellization, where instrument selection yields a truncated normal statistic amenable to a Mills-ratio correction, multivariable Rao-Blackwellization conditions on a noncentral $\chi^2$ statistic, for which no analogous correction is available. We derive closed-form conditional moments under this instrument selection model and use them to construct bias-corrected summary statistics that can be integrated into a wide range of existing MR methods. Simulations and real data analyses show that, when combined with methods such as MR-cML and MR-BEE, the proposed correction substantially improves type-I error control and yields more robust inference.

Perimenopausal women, often in the prime of their careers, make up a significant proportion of the workforce. Previous studies have revealed the significant symptoms burden associated with perimenopause, yet its workplace and economic consequences remain poorly understood. We examined work impairment by symptom severity and across reproductive stages in a cross-sectional survey of U.S. women aged 35-59 (n=945), using the Work Productivity and Activity Impairment questionnaire and Menopause Rating Scale. We then estimated associated productivity losses using a human capital approach. Perimenopausal women were equally likely to remain in the labour force as premenopausal women (76.6% vs. 78.0%) but reported substantially higher work impairment (22.5% vs. 12.7%). Work impairment rose from 3.4% among women with minimal symptoms to 33.4% among those with severe symptoms and was driven predominantly by presenteeism rather than absenteeism. Somatic and psychological symptoms showed the strongest associations with work impairment, whereas urogenital symptoms were not significantly associated. The observed work impairment translated into an estimated annual productivity loss of approximately $6,061 per woman in perimenopause and a societal burden of $56.7 billion in the United States. These findings suggest that perimenopause is a substantial but under-recognised workplace health challenge, requiring better recognition and tailored, symptom-matched, workforce support. Keywords: perimenopause, symptom burden, work impairment, productivity loss.