Emergency Medicine Journal

Objectives People with mental health conditions experience increased rates of diagnostic errors and delays in acute treatment. While causes such as diagnostic overshadowing (misattribution of physical symptoms to mental health conditions) are well documented, less attention has been paid to the organisational and structural conditions that shape diagnostic work. This study examines how physical illness is diagnosed in patients with mental health conditions in emergency departments (EDs), with a focus on the structural conditions that enable or constrain safe diagnostic practice. Method We conducted a multi-site ethnography across three purposively selected EDs in England between April 2023 and April 2024, varying in size, population demographics, and local service configuration. Data were collected through 284 hours of non-participant observation and 20 semi-structured interviews with ED staff. Results Our analysis identified four recurring structural gaps that shaped the conditions under which physical health diagnosis took place for patients with mental health conditions: a design gap, whereby targets and physical layouts constrained diagnostic reasoning; a preparedness gap, reflecting the lack of structural support to allow staff to act on their existing knowledge and skills; a coordination gap, reflecting fragmented ownership and the challenges of joint assessment across mental and physical healthcare teams; and an expectation gap, whereby unmet need elsewhere in the system increased demand for ED services that were beyond its formal scope. These gaps made diagnostic errors and delay more likely for patients with mental health conditions seeking physical healthcare in the ED. Conclusions As new dedicated mental health EDs are introduced in England, there is an opportunity to avoid reproducing these structural gaps in new settings. Our study suggests that improving physical healthcare for patients with mental health conditions requires changes to how EDs are designed, resourced and supported, and how they connect with the wider health and care system. Keywords: mental health, diagnostic inequality, emergency departments

INTRODUCTION Severe acute brain injury (stroke, traumatic brain injury or hypoxic-ischemic encephalopathy; SABI) is increasingly recognized as a chronic condition with care and communication needs beyond the initial hospitalization. This study aimed to characterize post-acute care patterns among SABI survivors, focusing on healthcare utilization and outpatient communication. METHODS Data were collected from a prospective cohort of hospitalized SABI patients using surveys, chart reviews, and the ED Information Exchange database. Socioeconomic disadvantage was assessed using the Area Deprivation Index (ADI), and qualitative analysis of outpatient notes examined conversations around palliative care needs and goals-of-care. RESULTS Two-thirds of patients (140/222) survived until discharge, primarily to nursing facilities (39%) or inpatient rehabilitation (38%). Among 109 with one-year follow-up, there were 89 hospitalizations, 104 ED visits, and 28 deaths. Patients from the most disadvantaged neighborhoods had significantly higher odds of rehospitalization or ED use within 30 days (OR 3.37, p=0.036). ADI was not linked to one-year utilization. seen outpatient by primary care (40%), neurology/neurosurgery (57%), and palliative care (1%), but conversations rarely revisited prognosis or goals-of-care. CONCLUSIONS Our findings highlight the need for improved long-term care planning and communication, particularly for socioeconomically disadvantaged survivors of SABI.

Background: Mass gathering events (MGEs) are associated with several public health challenges and may cause a strain on healthcare services. Literature findings on the impact of MGEs on emergency departments (EDs) are heterogeneous. Objectives: To examine shifts in ED attendance characteristics during a major sporting tournament, namely the UEFA European Football Championship 2024 held in Germany. Methods: We conducted a retrospective observational study using ED data from the Emergency Department Data Registry. We compared baseline ED attendance characteristics between the tournament and the reference period, defined as two weeks before and two weeks after the tournament, and between Germany game days and non-Germany game days. Hourly attendance patterns were analysed for all Germany games using a reference range. Results: We included data from 41 EDs, totalling 253,493 attendances during the study period. A 1.57% increase in attendance was observed during the tournament compared to the reference period, with baseline characteristics remaining similar. The median daily attendance within all EDs was slightly lower on Germany game days (4066) compared to non-Germany game days (4128). Modest changes were observed in the hourly attendance on Germany game days, most notable during the last Germany game where a decrease in attendance below the reference range extended over three hours. Conclusions: The observed shifts in ED attendance were minimal, suggesting that no major changes of public health relevance occurred in ED attendance during the tournament. We highlight the utility of using ED data for monitoring and for enhancing the understanding of the public health risks and challenges associated with MGEs.

Background Emergency department (ED) triage is a high-stakes clinical decision process that determines patient prioritization and resource allocation under time pressure. Large language models (LLMs) have recently been proposed as decision-support tools for triage, yet most evaluations rely on simulated scenarios or curated datasets. Evidence from real-world clinical environments remains limited. The objective of this project was to systematically evaluate the performance, calibration, and reproducibility of multiple contemporary large language models for Emergency Severity Index (ESI) classification and sectoral allocation (ED vs. urgent care practice, UCP) using a comprehensive real-world triage dataset. Material and Methods Retrospective cross-sectional benchmarking study conducted at a tertiary academic emergency ED in Germany with an integrated central point of assessment (CPA). The study included all consecutive adult walk-in encounters (>18 years) presenting between October 2023 and February 2024 (N = 16,107). Data were collected from a structured clinical decision support system capturing presenting complaints, vital signs, and triage decisions recorded by specialized nursing staff. Structured clinical variables routinely collected at triage, including presenting complaint categories (CEDIS-PCL), vital signs according to the ABCDE framework, and additional structured or free-text clinical information. Results The primary outcome was the agreement between LLM-predicted and nurse-assigned ESI levels measured using quadratic-weighted Cohen's k. Secondary outcomes included sectoral assignment agreement, misclassification patterns (over- and under-triage), calibration metrics, and output reproducibility. Quadratic-weighted k values ranged from 0.18 to 0.75 across models. Only a structured stepwise prompting strategy achieved substantial agreement (k_qw = 0.747), approaching reported human inter-rater reliability. Most models demonstrated moderate or lower agreement and systematic overconfidence, with expected calibration errors (ECE) based on verbalized confidence ranging from 0.099 to 0.355. Sectoral assignment agreement (i.e. ED vs. urgent care practice, UCP) was uniformly low (k < 0.30). Reproducibility testing revealed substantial variability in 23% of cases, indicating non-deterministic output behavior for clinically relevant decisions. Conclusions Current large language models demonstrate heterogeneous and generally limited performance in real-world emergency triage tasks. Structured algorithm-guided prompting appears more influential than model architecture or size. Before clinical implementation, improvements in calibration, reliability, and workflow integration are required, alongside regulatory-compliant validation in prospective clinical settings.

Objective To map national Parkinsons disease (PD) research capability to inform an inclusive delivery strategy for a large-scale clinical trial. Background Few people with PD participate in clinical trials, particularly from under-served populations. The Edmond J Safra Accelerating Clinical Trials in PD initiative (EJS ACT-PD) aims to deliver an inclusive multi-arm multi-stage (MAMS) disease modification PD trial. Methods A survey disseminated to National Health Service (NHS) hospitals assessed PD research capability regarding trial experience, rater expertise, trial facilities and specialist investigations. A process was developed to categorise sites into 3 tiers, with tier 1 having the least PD-research capability or experience, and tier 3 being experienced specialist centres. We mapped tiers to PD prevalence, social deprivation and ethnic diversity to identify infrastructure gaps. We developed trial delivery strategies to facilitate rapid and inclusive recruitment. Results Out of 97 survey responses, 43 sites were categorised as tier 1, 33 as tier 2 and 21 as tier 3. Diversity and social deprivation index were higher for tier 3 sites (predominantly urban). A greater proportion of tier 1 and 2 sites were situated in areas of higher PD prevalence (predominantly rural). Ninety one percent of sites reported experience with remote trial delivery methods. Our delivery strategy included: initial trial set-up at tier 3 sites to enable rapid and ethnically diverse recruitment; core funded staff within strategic sites to develop regional solutions for inclusive trial participation and to enable research opportunity provision in areas where currently very little exists, and a hybrid delivery model of in-person and remote study visits, ensuring maximal acceptability and deliverability. Conclusions The mapping of current PD research delivery capability has allowed us to develop a trial delivery strategy that will broaden the provision of research participation opportunity to under-served groups. It has also enabled existing infrastructure to be maximised while mitigating identified gaps.

Introduction: Blackpool, England's most deprived local authority, has the highest drug-related death rate in the country. People in police custody with problem substance use are a key Core20PLUS5 inclusion-health group, yet referral from the police into structured drug and alcohol treatment is fragmented and relies heavily on self-report. We evaluated the current police-to-treatment route in Blackpool and designed an evidence-informed unified pathway. Materials and Methods: A mixed-methods service evaluation and pathway-design project was conducted during a six-month General Practice / Public Health rotation. Routinely collected referral data from Horizon (the local specialist drug and alcohol service) covering the 47-month period from December 2019 to October 2023 were analysed. Findings were triangulated with national policy, the Project ADDER and Liaison and Diversion evaluations, and the international evidence on police-led pre-arrest diversion. Results: Of 5,900 total referrals into Horizon over 47 months, only 269 (4.56%) originated from the police. Police referrals accounted for fewer than 5% of monthly referrals in 30 of 47 months, for 5 to 9.9% in 16 months, and for >/= 10% in only one month (10.8%, December 2022). Blackpool recorded 76 drug-misuse deaths in 2019-21 (19.4 per 100,000, approximately four times the England rate). A six-step unified pathway is proposed: Initiate Referral (opt-out, from ADDER Police and Liaison and Diversion); Initial Assessment; Tailored Treatment Plan; Continuous Support; Collaboration and Monitoring; and Evaluation and Adjustment. Conclusions: Police contact is markedly under-used as a gateway to treatment despite Blackpool having the highest drug-related mortality in England. An opt-out, multi-agency pathway anchored in Core20PLUS5 has the potential to narrow the treatment gap, reduce re-offending, and address the structural health inequalities that drive premature mortality.

Objective To determine if it is possible to assess individual patient risk of the development of colorectal cancer (CRC) in people in high-risk groups due to their family history. Design/Method Retrospective observational study of prospectively collected data from consecutive patients referred for a colonoscopy. 2,478 consecutive patients were referred to a single colorectal surgical practice in Sydney, Australia between 1977 and 2018 for a colonoscopy because of a family history of CRC. Of these, 1,963 have been followed for more than 10 years and are the subject of this paper. Histopathological findings categorised as normal (N), non-advanced adenoma (NAA) or advanced neoplasia (AN) with AN proven to be the precursor to CRC. Intervention Colonoscopic screening on the basis of contemporary practice to 2006 and subsequently according to Australian National Health and Medical Research Council guidelines. Results Participants with normal or low-risk findings in the first decade remain at lower risk of CRC for 30 years from the commencement of screening. Conclusion It is possible to stratify individual patients in a high relative risk cohort into those with high or low personal risk of CRC based on colonoscopic findings in the first 10 years of surveillance. Those with no AN in the first ten years have a lower 30-year risk of developing AN than the general community. This offers the possibility of structuring surveillance programs around individual risk rather than group risk, lessening the need for multiple surveillance colonoscopies in the majority of such patients and improving the cost effectiveness of CRC screening at the population level.

Background. The emergency department in the United States of America functions as a residual access point for healthcare and social services for populations including rural communities, the uninsured, mental health and addiction patients, and the unhoused. The workforce variable that determines unit function (experience density, the concentration of accumulated clinical judgment within a unit workforce) is not measured in hospital accounting systems. Objective. To document workforce composition changes in U.S. emergency nursing across the 2018 and 2022 cycles of the National Sample Survey of Registered Nurses (NSSRN), and to specify falsifiable predictions for the 2026 cycle. Methods. We analyzed NSSRN public-use files using a four-way ED definition extending Castner et al. (2024) and a hospital-bedside-restricted comparator. Variance estimation used jackknife replicate weights for 2018 and Successive Differences Replication for 2022. Burnout was operationalized using the Norful et al. (2023) leaving-reasons proxy across cycles, with sensitivity analysis using the 2022 direct burnout item. Results. A 15-year trajectory (2008-2022) documents progressive experience-density compression: the ED's 15+ year veteran cohort fell from 41.9% to 28.0% over the decade preceding the pandemic, a loss of nearly a third of the senior cohort and a 19.6% decline in mean experience density, before recovering modestly to 33.3% as veteran nurses remained through the pandemic acute phase, leaving the ED as the youngest hospital setting throughout. Hospital non-ED bedside nurses lost senior tenure between cycles (mean 15.65[-&gt;]14.06 years since first licensure; 15+ year share 43.5%[-&gt;]38.7%), while ED nurses retained their senior tail (mean 11.60[-&gt;]12.58). Burnout endorsement rose sharply in both populations (non-ED 27.3%[-&gt;]46.0%; ED 34.2%[-&gt;]61.2%), with the ED-vs-non-ED gap more than doubling. Controlling for tenure, ED status was not independently associated with burnout in 2018 (OR 1.15, 95% CI 0.83-1.59) but was strongly associated in 2022 (OR 1.92, 95% CI 1.44-2.55; p<.001). The direct burnout item showed a parallel pattern (OR 2.92, 95% CI 1.62-5.28). Conclusions. A pandemic-era setting-specific burnout effect emerged in emergency nursing that workforce-composition controls cannot explain. The 2022 cycle establishes a pre-exit baseline against which the 2026 NSSRN will serve as the falsifiable test of post-Omicron veteran exit. Nursing pipeline replacement lag exceeds the interval before 2026 data arrives; the consequences of inaction fall on populations dependent on ED-based residual access.

Importance: As new treatments increase quality and length of life in people with multiple sclerosis (MS), effective prevention and management of common comorbidities, including Diabetes Mellitus (DM), is increasingly important. Objective: To compare incidence of DM and its associations with hospitalisation and mortality in adults with MS and matched controls. Design: Using English primary care data from the Clinical Practice Research Datalink (CPRD), linked to Hospital Episode Statistics and national mortality records, we matched adults with MS diagnosed between 2000 and 2023, with up to ten controls without MS by age, sex, and practice. We excluded individuals with preexisting DM, defined using diagnostic and management codes. Outcomes included all-cause hospitalisation (number and duration) and mortality. We used Poisson, negative binomial, linear, and Cox proportional hazards models, adjusting for demographic and socioeconomic factors, adding interaction terms to examine if ethnicity, deprivation, and urbanity were associated with outcomes. Results: We included 9,010 individuals with MS and 78,121 matched controls. Over a mean follow-up of 13.2 years, people with MS had over twice the incidence of DM compared with controls (adjusted incidence rate ratio [aIRR]=2.26, 95% CI: 1.96 to 2.61, p<0.001). Among people with MS, incident DM was associated with higher hospitalisation rates (aIRR=1.82, 95%CI: 1.47 to 2.28, p<0.001), longer hospitalisation duration (median 18 vs 4 days, adjusted beta;=0.53, 95%CI: 0.41 to 0.65, p<0.001), and increased all-cause mortality when incident DM was modelled as a time-varying exposure (adjusted hazard ratio=1.46, 95%CI: 1.17 to 1.82, p<0.001), compared to those who did not develop DM. Similar patterns were observed among controls (hospitalisation rates: aIRR = 2.96, 95% CI 2.63 to 3.23, p<0.001; hospitalisation duration: adjusted {beta} = 0.93, 95% CI: 0.86 to 0.99, p<0.001; mortality [time-varying]: HR = 1.50, 95% CI: 1.27 to 1.77, p<0.001). The relationship between DM and increased hospitalisation was stronger in rural areas among those with MS and stronger in White groups among controls. Conclusions: People with MS are more likely to be diagnosed with DM, resulting in greater all-cause hospitalisation and all-cause mortality. This highlights the importance of equitable screening, prevention, and management of DM in people living with MS, with particular attention to geographical health inequalities.

Background Electronic health record documentation patterns may reflect workflow complexity, monitoring intensity, and operational strain in intensive care settings. However, documentation-derived features can be sensitive to local documentation culture, data capture systems, and outcome definitions. Retrospective validation across multiple datasets is therefore needed before these signals are used in workflow intelligence or clinical AI governance tools. Objective To evaluate whether documentation-density and documentation-timing features show reproducible retrospective signal for ICU workflow complexity and long-stay proxy outcomes across de-identified critical care datasets, while distinguishing workflow and long-stay associations from unsupported claims about mortality prediction, burden reduction, or deployment readiness. Methods We synthesized retrospective validation results from de-identified ICU and workflow datasets generated through a prespecified documentation-density validation program. Feature families included Documentation Burden Score style features, Shift-End Documentation Rate style features, documentation reliability style metadata, and all-documentation feature sets where available. Outcomes included long ICU length of stay proxies, mortality where available, and workflow proxy endpoints. Models compared baseline feature sets with enhanced models containing documentation-density or workflow features. Performance was summarized using area under the receiver operating characteristic curve, Brier score where reported, delta AUROC, bootstrap confidence intervals where reported, and label-shuffle controls where available. Results The strongest external long-stay proxy evidence came from the NWICU chartevents analysis, which included 28,612 ICU stays, 20,267 stays with chart events, and 9,619,759 chart events. For ICU length of stay greater than the median, baseline AUROC was 0.5252. Enhanced AUROC was 0.9512 for Documentation Burden Score features, 0.9214 for Shift-End Documentation Rate features, 0.8470 for documentation reliability style features, and 0.9517 for all documentation features. Corresponding label-shuffle enhanced AUROCs were near random, ranging from 0.4897 to 0.5064. For ICU length of stay greater than the 75th percentile, baseline AUROC was 0.5155. Enhanced AUROC was 0.9433 for Documentation Burden Score features, 0.9194 for Shift-End Documentation Rate features, 0.8118 for documentation reliability style features, and 0.9427 for all documentation features, with label-shuffle enhanced AUROCs from 0.4836 to 0.4999. Additional retrospective support was observed in eICU workflow analyses, HiRID first-24-hour documentation-density analyses, MIMIC-IV HF ICU internal analyses, MIMIC-IV-Note metadata extensions, and nursing-chart or lab density proxy analyses. However, cross-institution discrimination transfer was weak without recalibration, and several analyses remained proxy validations rather than final clinical validations. Conclusions Documentation-density and documentation-timing features show promising retrospective signal for ICU workflow complexity and long-stay proxy outcomes, especially in NWICU chartevents and selected internal dataset-specific analyses. These findings support further preregistered, prospective, silent-mode validation of documentation-derived workflow intelligence. They do not establish prospective clinical performance, mortality reduction, clinician burden reduction, autonomous deterioration prediction, or deployment readiness.

Background: Medical registries have gained importance in the evaluation of healthcare quality outcomes. In the absence of high-quality evidence, such as randomized controlled trials, studies based on registry data are essential for informing clinical guidelines. Methods for assessing data quality are rarely described in detail. To ensure the credibility of registry-based studies, registries must use all available technical and operational means to guarantee high data quality. Method: Eurocrine(R) is a pan-European endocrine surgical database and quality registry initially funded by the EU healthcare programme, which started in 2015 and now includes more than 200,000 interventions as of April 2025. To ensure high data quality, interactive and standardized reports are created via Microsoft Power BI, which are created both centrally and locally. In addition, comprehensive data quality analyses were performed via the R-based package dataquieR. Results: Although a multitude of technical measures (for example, input screen design and real-time plausibility checks during data entry) are in place, they are not sufficient to prevent human errors at data entry. Errors identified in the reports were corrected, and preventive measures were implemented. Overall, the data quality was assessed as very good in terms of completeness, accuracy, and consistency. Conclusion: It is very important to provide registry users with an efficient and smart tool to identify data issues, as they have the clinical information to correct them. Data quality reports generated with dataquieR represent an effective tool for registry administrators. Predesigned Microsoft Power BI reports enable participating Eurocrine(R) clinics to self-audit their data.

Background and Purpose: Hemorrhagic transformation (HT) is a serious complication of endovascular thrombectomy (EVT), yet dedicated prediction models for young adults are lacking. We aimed to develop and externally validate a simplified risk score for HT in young adults with acute ischemic stroke undergoing EVT. Methods: This multicenter retrospective study included patients aged 18 to 49 years with acute anterior circulation large vessel occlusion who underwent EVT. The primary outcome was any HT within 24 hours after EVT. Multivariable logistic regression was used to identify independent predictors of HT, from which the NO?PAIN Score was derived. External validation was performed in an independent cohort of 138 patients. Results: Among 598 patients in the derivation cohort, HT occurred in 176 (29.4%). Five independent predictors were identified: admission NIHSS, number of thrombectomy passes, atrial fibrillation, alcohol consumption, and mTICI grade. The mTICI grade demonstrated a non-linear, inverted U-shaped relationship with HT risk, peaking at partial recanalization. The NO-PAIN Score showed acceptable discrimination in both the derivation (C-index, 0.737; optimism-corrected C-index, 0.748) and external validation cohorts (C-index, 0.726), with satisfactory calibration. Conclusions: The NO-PAIN Score is a simple risk prediction tool for HT after EVT in young adults with acute anterior circulation large vessel occlusion. It may assist in individualized risk stratification in this population.

Background: Statins are key to preventing atherosclerotic cardiovascular disease and lowering low-density lipoprotein cholesterol and cardiovascular events. However, skepticism regarding their safety and value persists and is increasingly influenced by social media. TikTok has emerged as a major source of health information, but its content varies in quality and accuracy. This study evaluated the quality, attitudes, misinformation, and engagement of statin-related content on TikTok. Methods: Public TikTok videos were collected using predefined search terms and coded by creator type, thematic content, and overall attitude. Video quality was assessed using the DISCERN instrument, the Patient Education Materials Assessment Tool for Audiovisual Materials, and the Global Quality Score. False or misleading claims were independently reviewed by two cardiology fellows. Associations between engagement and quality were also examined. Results: Of 1,349 screened videos, 258 met inclusion criteria. Most were educational (91.0%), with non-physician healthcare providers (34.5%) as the largest creator group. Risks or negative effects were discussed more often than benefits (63.2% vs 42.2%), and 39.5% contained at least one false or misleading claim, most often from complementary and alternative medicine providers and wellness promoters. Quality differed by creator type across all instruments, with physician-created content scoring highest. Video popularity showed minimal association with informational quality. Conclusion: Statin-related TikTok content frequently emphasizes harms, often contains misinformation, and varies substantially in quality by creator type. Greater involvement of healthcare professionals on social media may help improve digital health literacy and counter misleading information about statin therapy.

Introduction: Stroke care is time-sensitive, yet patients in low-resource settings may reach tertiary services only after passing through multiple formal and informal care options. This study examined documented care-seeking pathways and time to presentation among stroke cases recorded at the University of Medical Sciences Teaching Hospital (UNIMEDTH), Ondo State, Nigeria. Methods: A retrospective hospital record review was conducted using secondary data from the Stroke Registry, radiology department records, referral notes, and ambulance records at UNIMEDTH. The analysis included 371 stroke cases with documented time from symptom onset to UNIMEDTH presentation and reconstructable care pathways. First-contact routes were classified as hospital/biomedical, self/informal or traditional/faith-based care, and the number of documented steps defined pathway complexity before and including tertiary presentation. Frequencies and percentages described pathway patterns; median presentation times were compared using Mann-Whitney U and Kruskal-Wallis tests. Results: The median time to tertiary presentation was 24 hours (interquartile range [IQR] 9-72), and 317 patients (85.4%) presented after four hours. Only 30 patients (8.1%) presented directly to UNIMEDTH; 44 distinct care-pathway sequences were recorded. Hospital-facility first contact was documented for 81 patients (21.8%). It was associated with a median presentation time of 3 hours (IQR 2-6), compared with 48 hours (IQR 24-72) among patients whose initial contact was outside a hospital facility (U = 699.50, p < 0.001). The median time also differed across grouped first-contact categories and pathway complexity levels (both p < 0.001). Conclusion: Non-hospital or multi-step care-seeking pathways commonly preceded tertiary stroke presentations in this setting. The findings indicate that delayed tertiary arrival is partly embedded in the pathway followed after symptom onset. Interventions should combine public recognition of stroke warning signs with urgent referral linkages involving hospitals, patent medicine vendors, traditional and faith-based providers, and emergency transport systems.

Introduction Indigenous peoples in Canada face persistent health inequities rooted in colonialism, systemic racism, discrimination and social exclusion, all of which operate with particular intensity within healthcare institutions. Despite a growing qualitative literature documenting the discrimination and stigmatisation of Indigenous people by healthcare professionals, no validated instrument existed in the Canadian context to measure the stigmatizing attitudes and behaviors of clinicians toward this population. Aim This study aimed to co-develop and validate an instrument using clinical case vignettes designed to capture the affective, cognitive, and behavioral dimensions of stigmatization of indigenous peoples. Method Following Boateng et al.'s three-phase scale development approach, a multidisciplinary team including Indigenous patient partners, researchers, clinicians, and measurement experts generated 244 items across three paired clinical vignettes addressing type 2 diabetes, chronic back pain, and depressive disorder. Each vignette was developed in two versions, one featuring an Indigenous patient (test) and one featuring a non-Indigenous patient (control), distinguished solely by name and origin. Content validity was assessed by an expert committee using a Content Validity Index. The instrument was subsequently administered to a sample of nurses and physicians from two canadian health institutions using a twelve-arm randomization design. Analyses were carried to assess the internal structure of the instrument, convergent and concurrent validity as well as internal consistency. Results Our results show that the instrument developed has good psychometric qualities, particularly in terms of internal consistency, concurrent validity and factor structure, which reflects the theoretical structure assumed. Concurrent validity of the tool with the M-PATAS scale demonstrated weak to moderate significant correlations. Developed through a participatory process centering Indigenous expertise and lived experience, this instrument constitutes a significant methodological advance in the study of racialized stigmatization in Canadian healthcare.

Background Chronic pain is common in adults aged 85 years and older (85+) and is associated with detrimental outcomes. Chronic pain guidelines advise first line management with non-pharmacological measures; paracetamol and non-steroidal anti-inflammatory drugs are the preferred analgesics. Challenges in accessing non-pharmacological therapies for adults aged 85+, and the presence of multimorbidity and polypharmacy, mean that opioid medication is often prescribed for chronic pain despite the potential for opioid-related adverse effects and guidance identifying long-term opioids for chronic pain as a potentially inappropriate prescription. Aim This study aims to explore patient, caregiver, and healthcare professional perspectives on the prescription of opioid medications for pain management for chronic pain in adults aged 85+ to support development of resources for optimising opioid prescribing. Design and Setting In this qualitative study, participants were recruited through primary care, in the community or in care home settings. Method 36 semi-structured interviews were conducted with care home residents and community dwellers aged 85+ (n=12), caregivers (informal and care home staff) (n=12), and healthcare professionals (n=12). Interviews were transcribed and analysed using reflexive thematic analysis. Results Four themes were developed: contextual complexity, satellite influences, balancing act, and pragmatic prescribing. Using opioids in adults aged 85+ is a balancing act to support patients best possible quality of life within their unique circumstances whilst using the pain management tools available. Conclusion Opioids continue to have an important role in pain management in adults aged 85+ largely due to paucity of alternatives and the drive to support quality of life.

Background Despite existing road safety regulations, commercial motorcycle riders commonly referred to as "Boda Bodas" in Uganda continue to experience high rates of injuries due to road traffic accidents resulting from unsafe riding behaviours, contributing significantly to morbidity and mortality among both riders and passengers. Safe riding behaviours are less well documented, as well as factors associated with the observance of those behaviours. This study aimed to determine factors associated with safe riding behaviors for both boda-boda riders and their passengers in Kampala Central Division. Methods A cross-sectional survey study design was conducted using a convergent parallel mixed-methods design guided by the PRECEDE model. Quantitative data were collected from 424 riders through structured questionnaires administered by trained research assistants. Binary Logistic regression was used to determine the independent predictors of safe road riding behaviors, and Adjusted Odds ratios (AORs) have been reported. Data were analyzed using descriptive and inferential statistics, with a p-value <0.05 considered statistically significant. Qualitative data were collected simultaneously with quantitative data through in-depth semi-structured interviews with 10 passengers to capture perceptions of rider behaviors and safety practices. Thematic analysis was applied, and results were triangulated to highlight convergences and divergences between quantitative and qualitative findings, providing a comprehensive understanding of safety determinants for both riders and passengers. Results Of the 424 riders (mean rider age was 29.56 {+/-} 5.71), overall, 276 (65.1%) of riders exhibited unsafe riding behaviors. In the bivariate analysis with Logistic regression, predisposing factors (education, marital status, religion, and willingness to obey traffic regulations), and reinforcing factors (family encouragement) were significantly associated with safe riding behaviors. However, in the adjusted model, secondary (AOR=0.50; 95% CI:0.30-0.85) and post-secondary education (AOR=0.57; 95% CI:0.33-0.98), being married (AOR=0.56; 95% CI:0.34-0.91), Christian religion (AOR=2.98; 95% CI:1.63-5.47), willingness to obey traffic regulations (AOR=0.41; 95% CI:0.24-0.70), union advocacy (AOR=1.76; 95% CI:1.03-3.01), and well-maintained roads (AOR=1.65; 95% CI:1.07-2.55) were significant predictors of safe riding behaviors. Qualitative interviews further highlighted barriers to safety, including a lack of helmets, over-speeding, disregard for traffic regulations, and poor road infrastructure. Conclusions Rider and passenger safety is still low, interdependent, and influenced by multiple factors. Integrated interventions focusing on education, stronger families, religious affiliations, union safety advocacy, and stricter enforcement of traffic regulations are vital for enhancing safety for both riders and passengers.

Objectives: Study design indexing of biomedical publications is crucial for evidence retrieval and synthesis. We sought to evaluate the accuracy and suitability of a transformer-based model (TM) for indexing clinical study designs, in comparison to National Library of Medicine (NLM) indexing. However, this is challenging for at least three reasons: First, to date, all automated systems have been trained and evaluated on manual NLM indexing assignments, itself subject to errors. Second, TM's probabilistic predictive scores take into account uncertainty, and can be converted to TRUE/FALSE assignments in different ways depending on the needs of users, while NLM labels are categorical. Third, our goal (to tag articles only that exhibit a given design) differs from NLM which tags articles that both discuss as well as exhibit that design. Materials and Methods: Therefore, we carried out a limited evaluation of the TM model that focuses only on the articles that received the most confident predictions, that is, the highest scores that are almost certainly TRUE and the lowest scores that are almost certainly FALSE, but which disagreed with NLM assignments. This was performed both for articles published in 2016 (when NLM decisions were manual) and in 2025 (when NLM decisions were automated). To establish ground truth, dual annotators indexed the articles independently, following written definitions, for four prominent study designs--cohort, case-control, cross-sectional, and case report. Results: For three designs (case-control, case report, cross-sectional), the articles having the top 100 predictive TM scores (when NLM failed to assign that design) were judged to exhibit that design in the great majority (86-100%) of cases. Conversely, the articles having the lowest 100 predictive TM scores (when NLM did assign the study design) exhibited the design only in relatively few (0-21%) of cases. The most confident predictions of the TM model were highly accurate and not redundant with automated NLM indexing; the exception was cohort studies articles, in which both TM and NLM labels showed high error rates of both omission and commission. Discussion and Conclusion: TM may have value for identifying articles exhibiting study designs, which is especially important for clinical decision-making as well as systematic reviews and other evidence syntheses. NLM indexing of cohort studies cannot be regarded as a reliable gold standard for training or evaluation of automated systems, warranting efforts to create a new manually annotated corpus.

Background. Capillary refill time, an examiner-dependent bedside test of distal microvascular perfusion, has become a resuscitation target in septic shock,1,2,3,4 motivating a continuous surrogate computed from the photoplethysmogram (PPG, the optical waveform the pulse oximeter on every ICU patient already records).5,6,7,8 Objective. We attempted three PPG-derived candidate measures on the MIMIC-IV Waveform Database (MIMIC-IV-WDB v0.1.0) and asked, by inspecting randomly drawn examples, whether each captured its intended physiology before any downstream modeling. Methods. MIMIC-IV-WDB v0.1.09 was linked to MIMIC-IV.10 The signals were a cuff-anchored perfusion-index recovery (reactive hyperemia when the cuff shares an arm with the probe), a slow Mayer-wave-band power ratio of the perfusion index (sympathetic vasomotor tone), and a per-beat diastolic exponential decay time constant (a refill-like recovery time). For each signal we drew 10 random examples at a fixed seed and checked them against a checklist fixed in advance. Each was read by the author and, separately, by MedGemma 1.5, a multimodal medical language model run locally. A synthetic test with a known time constant checked the third signal. Results. The cuff-anchored signal showed the expected occlusion-reperfusion shape on 268 of 6,236 evaluable cuff cycles (4.30%) in 15 of 19 patients, consistent with opposite-limb placement of the probe and cuff. The slow-band ratio returned a stable cohort value, but a clear, stationary peak appeared in only4 of 10 random windows. The per-beat fit met its goodness-of-fit threshold in 10 of 10 beats, yet a cardiac-frequency heuristic flagged a possible fit on the heart-rate oscillation in 7 of 10, and in 5 of 17 patients the time constant lay where an exponential is indistinguishable from a straight line. A 0.5Hz high-pass pre-filter implanted its own approximately 318 ms time constant regardless of truth. The language model tracked the human on clear positives but reported the pattern present on every call it returned, never absent. Conclusions. Two of the three candidate signals did not reflect their intended physiology in most examples, and the third was constrained by sensor placement. Inspecting a few random raw inputs against a checklist written in advance is an inexpensive upstream check before downstream inference on PPG-derived microvascular signals.

Objective: Clinical phenotyping methods that rely on clinical and informatics expertise can be time-intensive and costly. We tested both manual and highly automated approaches using electronic health record (EHR) data to identify an FDA Sentinel Initiative health outcome of interest, acute pancreatitis. Materials and Methods: We trained and evaluated machine learning algorithms using EHR data with two approaches: a custom approach that included manually curated features and trained on outcomes data validated with medical record review, and a highly automated approach that greatly simplifies and automates feature engineering and relies on low-cost silver-standard outcomes for model training. Results: Custom algorithms using manually curated structured claims data discriminated cases from non-cases with a high degree of accuracy (cv-AUC 0.89 [95%CI 0.84-0.94]); the inclusion of natural language processing (NLP)-derived covariates from clinical notes increased performance slightly (cv-AUC 0.91[95%CI 0.86-0.97]). The automated algorithm trained on the outcome count of diagnosis codes performed less well (AUC 0.80 [95% CI 0.75-0.85]) but improved using maximum lipase value as an outcome (AUC 0.88 [95% CI 0.84-0.92]). At a positive predictive value of 90%, the custom algorithm had a sensitivity of 92%, the automated algorithm trained on diagnosis code count had a sensitivity of 45%, and the automated algorithm trained on maximum lipase value had a sensitivity of 84%. However, a prediction rule derived by clinicians during chart review was nearly as accurate (maximum lipase value [&ge;] 3 times upper limit of normal; AUC 0.86, PPV 85%, sensitivity 92%). Discussion: Machine learning algorithms with manually curated structured data and NLP features trained on validated outcomes data successfully identified validated events. Use of an outcome in the automated model based on specific phenotype knowledge (maximum lipase value) allowed for performance similar to the custom model and with considerably less resources.