BJGP Open

Aims: To understand how users believe continuous glucose monitoring (CGM) changes their behaviour, which app features they credit, and whether endorsing more mechanisms of action (MoAs) is linked to reported diet and physical activity change. Methods: We surveyed 112 U.S. adults with type 1 or type 2 diabetes who had used a Dexcom or Abbott Libre CGM within 12 months. Participants rated 26 MoA statements mapped to the Capability, Opportunity, Motivation-Behaviour (COM-B) model, identified contributing app features, and reported CGM-attributed diet and physical activity change. Logistic regression examined MoA count and COM-B domains as predictors of behaviour change. Results: Participants (mean age 54.5 years; 60.7% female) reported dietary (82.1%) and physical activity (46.4%) change and endorsed a median of 18 MoAs, most often relating to knowledge, skills and behavioural consequences. Real-time glucose and graphs were attributed to many MoAs. Each additional MoA was associated with higher odds of dietary (OR 1.27, 95% CI 1.15-1.44) and physical activity change (OR 1.27, 95% CI 1.16-1.43). Motivation made the strongest distinct contribution among COM-B domains. Conclusions: Users perceive CGM as engaging many behavioural mechanisms; endorsing more was linked to behaviour change. Motivation may be a key target for CGM-based behavioural support.

Abstract Background: Respiratory failure (RF) and atrial fibrillation/flutter (AF/AFL) often occur together in older adults, but long-term national mortality trends for the two conditions combined have not been described. We examined trends and disparities in deaths with co-listed RF and AF/AFL among U.S. adults aged 65 years and older from 1999 to 2024, and projected rates through 2035. Methodology: We used the CDC WONDER Multiple Cause of Death database to identify deaths with ICD 10 codes J96 (RF) and I48 (AF/AFL) listed together. Age-adjusted mortality rates (AAMRs) per 100,000 were standardized to the 2000 U.S. population and stratified by sex, race/ethnicity, census region, urbanization, state, and place of death. Joinpoint regression was used to estimate annual percent changes (APCs) and average annual percent changes (AAPCs). Forecasts for 2025 to 2035 were generated with an exponential smoothing (ETS) model. Results: A total of 433,598 deaths were recorded. The overall AAMR rose from 12.8 in 1999 to 66.7 in 2024 (AAPC 8.11%; 95% CI, 7.35 to 8.87; P < 0.0001). Three segments were identified: an increase from 1999 to 2018 (APC 7.73%), a sharp rise from 2018 to 2021 (APC 18.10%), and a decline from 2021 to 2024 (APC -5.25%). Males had a higher AAMR and a steeper increase than females (AAPC 8.44% vs 7.81%). Non-Hispanic White adults had the highest mean AAMR, Hispanic adults had the largest relative increase (AAPC 8.72%), and Non-Hispanic Asian/Pacific Islander adults had the smallest (AAPC 6.23%). The Midwest had the fastest regional growth (AAPC 9.35%). From 1999 to 2020, non-metropolitan areas increased faster than metropolitan areas (AAPC 10.09% vs 8.21%). State AAMRs ranged from 24.7 in Ohio to 85.1 in Louisiana. Most deaths occurred in hospital inpatient settings (64.1%), followed by nursing homes (14.1%) and the home (11.9%). The model projects an AAMR of 122.96 per 100,000 by 2035 (95% prediction interval, 85.42 to 160.50). Conclusions: Mortality involving RF with AF/AFL among older U.S. adults increased more than fivefold over 25 years, with a marked surge during the COVID 19 period and only a partial decline afterward. Differences by sex, race/ethnicity, region, and urbanization point to groups that may benefit from earlier detection and coordinated cardiopulmonary care.

Background Cardiac amyloidosis is an increasingly recognized cause of heart failure in older adults, yet Colombian data, and data from the southwest of the country in particular, remain scarce. We describe the clinical, diagnostic, imaging and outcome profile of patients with cardiac amyloidosis at two high-complexity institutions in Cali. Methods Observational, descriptive, retrospective, two-center study. Patients were identified in the institutional registries of both centers using ICD-10 codes for amyloidosis, and clinical data were abstracted manually from medical records. Continuous variables are reported as median (interquartile range) and categorical variables as counts and percentages over the number of patients with available data. Results Thirty-two patients were analyzed (median age 77 years; 68.8% men). Heart failure (90.6%), atrial fibrillation (46.9%) and carpal tunnel syndrome (31.2%) were frequent. The etiological classification depended on the test considered: bone scintigraphy was positive (Perugini 2-3) in 23 of 26 patients studied (88.5%), but a monoclonal protein was screened for in only 22, and the full non-invasive criteria for ATTR were met in 16 of 32 (50.0%). Genetic testing was positive in 9 of 18 tested, and the variant was Val142Ile in all 8 patients in whom it was characterized. The working diagnosis was ATTR in 26 patients (81.2%) and AL in 6 (18.8%). Tafamidis was used in 37.5%. Eleven of the 31 patients with known vital status died (35.5%), and NT-proBNP was markedly higher among those who died (6640 vs 1186 pg/mL; p=0.003). Conclusions In this southwestern Colombian cohort, cardiac amyloidosis presented as the classic transthyretin phenotype, with the Val142Ile variant in all 8 patients in whom it was characterized. Bone scintigraphy was frequently positive, but screening for a monoclonal protein was often omitted, so only half the cohort met the full non-invasive criteria for ATTR. Mortality was high and closely associated with NT-proBNP.

Objective: To estimate how often remission of type 2 diabetes is recorded in routine care, how long it lasts, and what ends it, separating a return of hyperglycemia from a restart of treatment. Research design and methods: We studied 136,781 adults aged 18 years or older with laboratory confirmed type 2 diabetes in the Protege linked electronic health record (EHR) and claims database. The 96,817 adults with glucose-lowering treatment recorded at or before cohort entry formed the primary (previously treated) cohort; 39,964 adults with no treatment recorded at entry were analyzed in parallel. Remission required two HbA1c values <6.5%, 90 to 365 days apart, the first at least 3 months after all glucose-lowering drugs were discontinued, with no therapy recorded between them. After remission we followed glycemic relapse (HbA1c over 6.5%) and treatment restart (a new outpatient prescription or dispensed fill) separately, with death as a competing event (Aalen Johansen). Results: Remission was documented in 4.33% of previously treated adults cohort and 15.57% in the cohort of untreated adults at entry. Five-year cumulative incidence (Aalen Johansen) was 3.82% (95% CI 3.69 ;3.93) and 13.27% (12.90;13.59) in each cohort, respectively. Remission was less common with greater baseline treatment burden: exploratory adjusted 5-year incidence was 4.65% in adults with 1 glucose-lowering class with a treatment episode recorded at or before entry, 3.62% with two, 2.69% with three, and 2.02% with four or more such drugs.. The Median Kaplan Meier duration of first remission was 226 days (IQR 93 to 855) in previously treated adults and 371 days (166 to 1,027) in adults untreated at entry; 37.9% (treated cohort) and 23.1% (untreated cohort) of episodes had ended by 152 days. Among remission episodes that had ended, median duration was 159 days (71 to 362) and 265 days (129 to 565) for the treated and untreated at entry cohort, respectively. Within 5 years of remission in previously treated adults, the first event ending remission was glycemic relapse in 31.94%, treatment restart in 47.83%, and death in 4.65%.Treatment restart was about 1.5 times as common as glycemic relapse as the first event ending remission. A second remission followed within 5 years in 19.53% of previously treated and 24.10% of untreated remitters. Conclusions: Recorded remission was uncommon and short-lived, and it ended more often with a treatment restart than with a rise in HbA1c. Studies of remission in real-world data should report relapse and restart separately and state how they confirm remission and establish that treatment stopped.

Awareness of comorbidities in people with dementia is vital context for dementia research and clinical management. Natural Language Processing (NLP) applied to free text in the electronic health record (EHR) may help ascertain comorbidity presence. Our aim was to assess the validity of the MedCAT Comorbidity NLP app in a cohort of people with dementia. We used text from the EHR of South London and Maudsley NHS Mental Health Trust via Clinical Records Interactive Search (CRIS), linked to structured data from local primary care (Lambeth DataNet) and hospital admission data in a cohort of 3,543 people with dementia. We evaluated MedCAT Comorbidity, examining internal validity via manual review of text and external validity by cross-validation against linked primary care and hospital data; and comparison of the predictive value of comorbidity measures for mortality. Internal precision was highest for atrial fibrillation (100%) and lowest for diabetes (78%). Cross-validation with linked data varied substantially across conditions, with precision from 96% to 59% and recall 77% to 4%. The median number of comorbidities identified was two using any single source, or three when combined. MedCAT Comorbidity demonstrated predictive ability for mortality (HR 1.11 per condition), although the model fit was better using comorbidity from hospital admission data. In people with dementia, the MedCAT Comorbidity app showed high precision for most conditions during internal validation. The variability of precision and recall on external validity assessment partly reflects incomplete documentation. Results from the MedCAT comorbidity app can assist researchers in understanding comorbidity of cohorts, particularly when primary care or hospital data is not available. Our findings highlight the need to develop quality assurance protocols that include internal and external validity checks before deploying general data extraction apps for specific clinical subpopulations.

Importance: Conditions that are hard to diagnose, several more common in women, may be missing from electronic health records (EHRs), hiding patients from research cohorts and case finding. Objective: To measure how often the EHR contains a diagnosis code for chronic conditions that participants report, and to compare this with national survey benchmarks. Design, Setting, and Participants: This cross-sectional study used the All of Us Research Program Registered Tier Dataset v9. Participants were adults with EHR visits in both 12-month periods from July 2021 to June 2023 who completed a health history survey. We analyzed the data in October 2026. Exposures: Self-reported clinician diagnosis of 10 conditions with ongoing visits or treatment, as well as sex at birth, race and ethnicity, age, and years of EHR records. Main Outcomes and Measures: Share of participants reporting current care who had a qualifying diagnosis code, alone and relative to hypertension and type 2 diabetes, and the ratio of age-standardized coded shares to national survey prevalence. Results: Of 161,480 respondents, 114,610 (73,870 female [64.5%], median [IQR] age 63 [50-72] years) had at least 5 years of EHR records before the survey. Among those reporting current care, 84.6% (95% CI, 84.2%-85.0%) with hypertension and 87.0% (95% CI, 86.5%-87.6%) with type 2 diabetes had a code, compared with 41.3% (95% CI, 38.9%-43.8%) for endometriosis, 39.4% (95% CI, 34.3%-44.8%) for Lyme disease, and 24.4% (95% CI, 23.2%-25.6%) for chronic fatigue. Counting ICD-9-CM and other mapped codes, 55.3%, 51.5%, and 28.3%, respectively, had any qualifying record. Women had lower coded shares than men for hypertension, type 2 diabetes, obesity, depression, and anxiety (crude ratios 0.93-0.96, within-health-system ratios 0.98-1.02) and higher shares for migraine and fibromyalgia (within health systems, 1.11 [95% CI, 1.07-1.15] and 1.41 [95% CI, 1.29-1.57]). Age-standardized coded shares among active patients were 1.1 to 2.4 times national prevalence of the same self-reported diagnoses. Conclusions and Relevance: In this cross-sectional study, fewer than half of participants reporting ongoing care for chronic fatigue, Lyme disease, or endometriosis had a qualifying ICD-10-CM code. Linking each participant's report to their own record measured this gap directly.

Purpose Prescribing to lactating women is constrained by limited evidence. Published compatibility classifications provide clinical guidance and a discrete research comparator. How often prescribing diverges from that guidance, and what characteristics are associated with divergence, is unknown at national scale. Methods Retrospective analysis of prescriptions filled during lactation, identified in the 2012-2014 MarketScan Commercial Claims Database. Prescriptions were classified as concordant (Hale's lactation risk category L1-L3) or discordant (L4-L5). Multivariable logistic regression identified factors associated with concordance. Results Of 96,484 new prescriptions, 95,710 (99.2%) were concordant and 774 (0.8%) were discordant. Discordant prescriptions were filled later after delivery (median 59 vs 33 days) and had a day supply three times longer (30 vs 10 days). In multivariable analysis (N=28,732), concordant prescriptions were associated with maternal age under 35 (adjusted odds ratio (aOR)=1.34, 95% confidence interval (CI): 1.02-1.75) and chronic-condition medications (aOR=10.94, 95% CI: 6.47-18.50), and were less likely within 14 days of delivery (aOR=0.53, 95% CI: 0.38-0.73) and with each additional day of supply (aOR=0.96, 95% CI: 0.96-0.96). Compared with family practice, concordance did not differ for obstetric, internal medicine, or emergency medicine prescriptions, but prescriptions from other provider types had lower odds of concordance (aOR=0.57, 95% CI: 0.41-0.79). Conclusions Prescribing during lactation was overwhelmingly concordant with published compatibility guidance; the rare divergence was patterned by prescription and prescriber characteristics. Because the categories are an expert classification rather than consensus guidance, divergence should not be read as inappropriate prescribing.

Type 1 diabetes mellitus (T1DM) remains a growing global health priority, with disproportionate underdiagnosis, delayed diagnosis, and mortality in sub-Saharan Africa. Despite the introduction of the Life for a Child (LFaC) programme in Kenya, which provides free insulin, glucose-monitoring supplies, and specialist education, no published studies have examined the determinants of glycaemic control among Kenyan children and adolescents with T1DM. This study aimed to determine the proportion of children and adolescents achieving good glycaemic control and to identify the socio-demographic and clinical determinants of glycaemic control at a tertiary hospital in Kenya. This cross-sectional study enrolled 160 children and adolescents with T1DM attending the paediatric diabetes clinic at Kenyatta National Hospital, Nairobi, between September 2025 and May 2026. Sociodemographic and clinical data were collected through structured interviews and physical examination. Poor glycaemic control was defined as HbA1c >7.5%. Associations between candidate determinants and glycaemic control were assessed using chi-square or Fisher's exact tests. Variables with p<0.2 on bivariate analysis were entered into a multivariable logistic regression model; Firth's penalized-likelihood method was used given the small number of participants achieving good control. Only 9 participants (5.6%) achieved the target HbA1c of [&le;]7.5% (mean HbA1c 11.02%, SD {+/-}2.29), and only 5 of 133 patients (3.8%) had [&ge;]70% of glucometer readings within the target blood glucose range (3.9-10.0 mmol/L).On bivariate analysis, household income (p=0.026) and self-monitored blood glucose (SMBG) frequency (p=0.018) were significantly associated with glycaemic control. In an exploratory multivariable regression analysis (Firth penalized logistic regression, given the small number of participants with good control), only SMBG frequency [&ge;]4 tests/day remained independently associated with lower odds of poor control (adjusted OR 0.19, 95% CI 0.04-0.84, p=0.028); the effect of household income was attenuated after adjustment. Glycaemic control remains markedly poor among children and adolescents with T1DM in this Kenyan tertiary hospital setting despite structured programmatic support. Frequency of self-monitoring, rather than household income alone, was the factor most strongly associated with glycaemic control, suggesting that strengthening access to glucose-testing supplies and supporting their consistent use should be prioritised in care, alongside attention to socioeconomic barriers. Longitudinal studies are needed to confirm these associations.

Background. Immune checkpoint inhibitors (ICIs) can cause immune-related adverse events in almost any organ. While myocarditis has received the most attention, data on pericardial disease with ICI use are limited. We examined pericardial adverse event reporting with nine ICIs using the FDA Adverse Event Reporting System (FAERS). Methods. We combined legacy AERS and current FAERS quarterly files from January 2011 to June 2026 into 18,629,240 unique cases, deduplicated by case number, and removed cases the FDA had listed as deleted. ICI exposure was identified from generic names, brand names, development code names and the active-ingredient field. Pericardial events were defined by seven MedDRA preferred terms. We estimated reporting odds ratios (ROR), proportional reporting ratios (PRR), Bayesian information components (IC) and empirical Bayes geometric means (EBGM). We also examined myocarditis co-reporting, time trends, a comparison with other antineoplastic agents, a MedDRA query-based outcome, and 14 alternative definitions. Results. The analysis included 18,514,045 cases: 277,434 involved an ICI, 34,792 carried a pericardial term, and 1,680 had both. Pericardial events were reported disproportionately with ICIs (ROR 3.35 (95% CI 3.19 to 3.52); PRR 3.34; IC025 1.62). Eight of nine agents had a ROR above 1 (3.32 to 5.63). Among the most frequently reported terms, estimates were highest for cardiac tamponade (5.59 (95% CI 4.98 to 6.28)), followed by pericardial effusion (3.83 (95% CI 3.60 to 4.07)) and pericarditis (2.09 (95% CI 1.89 to 2.31)). After removing cases that also reported myocarditis, the ROR was 3.13 (95% CI 2.98 to 3.30). Most sensitivity analyses gave similar estimates (ROR 3.13 to 3.47); defining exposure by generic drug names alone, which identified only 44% of exposed cases, gave 3.99. Compared with cases naming other L01 antineoplastic agents, the ROR was 1.13 (95% CI 1.08 to 1.19): 1.28 (95% CI 1.20 to 1.36) for ICI-only cases and 0.96 (95% CI 0.89 to 1.04) when another antineoplastic was also named. The ICI-specific reporting odds ratio fell each quarter (odds ratio 0.977 per quarter, 95% CI 0.973 to 0.980). Conclusions. In FAERS, pericardial events were reported more often with ICIs than with other drugs across most agents and terms; however, incidence and causation cannot be determined. The excess over other antineoplastic agents was small, and reporting disproportionality declined over time. These findings support clinical awareness of pericardial disease in patients receiving ICIs and warrant study in data that can measure incidence.

Background: Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) are widely used for EGFR-mutated non-small cell lung cancer (NSCLC) but have distinct toxicity profiles across generations. Aims: To investigate relationships between EGFR-TKI physicochemical, pharmacokinetic and pharmacological properties and drug-specific adverse drug reactions (ADRs) using national and global pharmacovigilance data. Methods: ADRs for erlotinib, afatinib and osimertinib were extracted from the UK MHRA Yellow Card scheme and WHO VigiAccess. Disproportionality analysis used reporting odds ratios (RORs). Signals were defined as ROR >2, lower 95% confidence interval >1 and >3 cases. Drug properties were curated from ChEMBL, emc, SmPC and EMA sources. Results: VigiAccess contained 88,662 individual case safety reports (163,619 ADRs) and Yellow Card contained 752 reports (1,814 ADRs). Four System Organ Class (SOC) signals were identified in VigiAccess and three in Yellow Card, with five additional Yellow Card Preferred Term signals. Eye disorders showed the strongest consistent association with erlotinib (VigiAccess ROR 4.33; Yellow Card ROR 4.18). Osimertinib was associated with pneumonitis (ROR 7.67) and pulmonary embolism (ROR 3.94). Afatinib showed psychiatric disorder signals in both databases (ROR 3.2-3.3) and diarrhoea (ROR 2.79). Neoplasm-related signals likely reflected confounding by indication. Conclusions: EGFR-TKIs demonstrate generation-specific pharmacovigilance profiles. Integration with drug properties supports mechanistic plausibility for several signals, while the atypical afatinib psychiatric association warrants further investigation. Findings are hypothesis-generating and require confirmation in controlled observational or mechanistic studies.

Background. Real-world 12-lead electrocardiograms (ECGs) often have missing or corrupted leads. A deep-learning classifier can be prepared for this by random lead masking during training, or by injecting clinical knowledge, for example by pulling its attention towards the leads that guidelines use for each diagnosis. Whether this knowledge adds robustness beyond masking is unknown. Methods. Pre-registered comparison of three arms sharing one network (per-lead 1D-ResNet encoder and per-diagnosis attention over the 12 leads): standard training (M0); random whole-lead masking (M1); masking plus a Kullback-Leibler penalty pulling attention towards a fixed guideline-derived key-lead map (M2, guideline-based attention guidance). Six diagnoses; five seeds per arm. Development on PTB-XL (21,799 ECGs); single sealed evaluation on PTB-XL fold 10 (2,198 ECGs) and on CODE-15% (233,204 exams, external validation). Primary endpoint: AUROC difference M2 - M1, averaged over first-degree AV block, right and left bundle branch block and atrial fibrillation and over 1 to 6 lost (zeroed) leads, with a patient-level paired BCa bootstrap 95% confidence interval. Results. The guidance did not change robustness: {Delta}AUROC -0.0010 (95% CI -0.0033 to +0.0006), an interval entirely within the pre-specified +-0.02 margin; the result was unchanged with another bootstrap seed, other interval methods and leave-one-diagnosis-out analyses. Masking improved AUROC: M1 - M0 = +0.0134 (95% CI +0.0102 to +0.0172) when one key lead was lost, and the number of lost leads tolerated before AUROC fell by more than 0.02 rose from 7 (M0) to 10 (M1). M2 attention matched the map and M2 was non-inferior on intact ECGs, but its calibration slope was lower for all six diagnoses in both datasets. M2 lost slightly more when all key leads were lost together (-0.0067) and gained only when one to three leads remained (exploratory; AUROC 0.9408 vs 0.9210 with 11 leads lost). CODE-15% reproduced every pattern (primary -0.0007, 95% CI -0.0011 to -0.0003). Conclusions. Random lead masking suffices to make an ECG classifier robust to missing leads; adding guideline knowledge aligns attention with clinical criteria but adds no robustness and worsens calibration. Guideline-aligned attention should not be taken as evidence of robustness.

Background: Cardiovascular (CV) screening is critical for personnel deploying to Antarctica, where geographic isolation and limited medical care make the management of acute cardiovascular events challenging. The utility of contemporary risk prediction tools such as the AHA PREVENT equations in this unique population remains uncertain. Methods: We retrospectively analyzed 154 individuals referred for enhanced CV evaluation before deployment to the U.S. Antarctic Program (USAP). Participants were evaluated across three temporal cohorts (2018-2020, 2021-2023, and 2024-2026). Demographics, CV risk factors, cardiac testing, and deployment clearance outcomes were collected. Risk was estimated using the Framingham Risk Score (FRS), AHA/ACC Pooled Cohort Equations (PCE), and AHA PREVENT risk models. Results: The cohort was predominantly male (79.9%; mean age 44.6 {+/-} 14.6 years), and abnormal ECG was the most common referral trigger (70.8%). Median 10-year risk was low by PREVENT and FRS and borderline elevated by PCE (PREVENT 2.7% [IQR 1.1-6.6], FRS 2.3% [IQR 0.5-7.1], and PCE 6.1% [IQR 2.4-9.2]). Among 72 participants with all scores, 51% were classified discordantly; PREVENT ASCVD agreed closely with FRS (=0.77) but poorly with PCE (=0.24-0.35), which consistently estimated higher risk. PCE was calculable in 52.6% versus 70.8% for PREVENT. Overall, 132 participants (85.7%) were cleared for deployment, while 8 (5.2%) were not physically qualified (NPQ); 6 of these NPQs were due to cardiovascular causes. Of 22 cardiac medical evacuations during the study period, 21 involved individuals without prior advanced cardiovascular screening. One screened individual was evacuated for symptoms subsequently found to have no cardiac cause. Conclusions: Risk calculators disagree substantially in the fitness-selected population. We recommend PREVENT as the primary risk-stratification tool as it is the most contemporary derivation, incorporates renal function, is race-agnostic, extends to heart failure, applies from age 30, and agrees closely with FRS, easing transition. Targeted imaging, including coronary artery calcium scoring, may supplement intermediate-risk candidates.

Aims Rosuvastatin is commonly co-administered with ticagrelor in patients with acute coronary syndrome. Ticagrelor inhibits breast cancer resistance protein (BCRP/ABCG2), increasing rosuvastatin exposure and potentially the risk of rhabdomyolysis. This risk may be further influenced by age and the ABCG2 c.421C>A variant. We aimed to predict optimal rosuvastatin dosing with ticagrelor in elderly patients stratified by ABCG2 genotype using physiologically based pharmacokinetic (PBPK) modelling Methods PBPK models for rosuvastatin and ticagrelor were developed and validated in PK-Sim using clinical data from healthy adults, elderly patients, and individuals with wild-type or homozygous ABCG2 c.421C>A genotypes. Key parameters, including BCRP catalytic activity and age-dependent declines in CYP2C9 and CYP3A4 activity, were optimized. The drug-drug interaction model incorporated competitive BCRP inhibition by ticagrelor. Rosuvastatin steady-state exposure was simulated with half or quarter standard doses during ticagrelor co-administration, and exposure matching approach was used to identify optimal dosing. Results The PBPK models demonstrated good predictive performance across populations and genotypes. In young adults with the wild-type genotype, halving the rosuvastatin dose resulted in an AUC fold change of 1.44, supporting a half-dose strategy. In young homozygous variant carriers, half-dose co-administration produced comparable exposure (AUC fold change 0.78). In elderly wild-type individuals, a four-fold dose reduction achieved comparable exposure (AUC fold change 1.06), whereas elderly variant carriers required only a half-dose reduction (1.12). Conclusions PBPK modelling supported age- and ABCG2 genotype-specific rosuvastatin dosing with ticagrelor. Elderly wild-type patients may require a further dose reduction to minimise toxicity while maintaining therapeutic exposure.

Objective: to estimate reconsultation risk and trajectories after a new episode of musculoskeletal pain in children and young people (CYP), and to determine routinely recorded prognostic factors associated with reconsultation. Design: Population based retrospective cohort study. Setting: Clinical Practice Research Datalink (CPRD) Aurum, 2005-2021. Participants: CYP aged 8-18 years consulting primary care for a new musculoskeletal pain episode (no musculoskeletal consultation in prior 12-months). Main Outcome Measures: Musculoskeletal pain reconsultation within 12-months, and 3-year musculoskeletal reconsultation trajectories derived using latent class growth analysis. Prognostic factors selected from a literature search and two-year symptom histories (sample prevalence >1%) were examined using regression models (conditional logistic; multinomial). Results: Among 313,830 CYP with >12 months' follow-up, 32% reconsulted for musculoskeletal pain within 12 months. Among 240,384 CYP with >3 years' follow-up, three trajectories were observed: single episode (62%), early reconsultation (18%), and persistent reconsultation (20%). Older age; female gender; hip, knee, multisite and back pain on index; previous headache; previous abdominal pain; and analgesic prescribing were associated with higher reconsultation risk. Asian or Black ethnicity, and residing in more deprived areas, were associated with lower recorded reconsultation. Conclusion: Most CYP had no further recorded consultation, but 38% followed early or persistent reconsultation trajectories. Routinely recorded prognostic factors, particularly index pain side and prior pain/symptom history, may differentiate groups who could benefit from proactive follow-up. Lower recorded reconsultation among Asian and Black individuals, and living in more deprived areas, should not imply lower need and warrants equity-sensitive monitoring of access, health-seeking, and coding.

Background: Artificial intelligence (AI) may improve electrocardiographic differentiation of Takotsubo syndrome from acute anterior ST-segment elevation myocardial infarction (STEMI), but its impact on human diagnostic performance, the added value of visual explanations, and differences by reader background and experience remain uncertain. Methods: Using 12-lead electrocardiogram (ECG) images from two hospitals, we developed and externally validated an ensemble of three deep learning models after self-supervised pretraining and fine-tuning. The diagnostic task used ECGs recorded within 14 days after symptom onset, regardless of reperfusion status. We conducted a four-phase reader study. Phases 1-3 compared unaided interpretation, a reference sheet, and additional AI assistance. In Phase 4, readers evaluated a new ECG set after randomization to reference-sheet support alone, with AI predictions, or with AI predictions plus gradient-weighted class activation mapping (Grad-CAM) heatmaps. Diagnostic correctness was analyzed using generalized linear mixed-effects models. Exploratory analyses examined differences by cardiologist status and experience. Results: Development and external-validation cohorts, including healthy controls, comprised 832 and 95 participants, respectively. For Takotsubo-STEMI discrimination, external-validation accuracy was 0.914, with areas under the receiver operating characteristic and precision-recall curves of 0.970 and 0.984. Seventy healthcare providers completed the reader study. The estimated probability of correct diagnosis increased from 0.632 without assistance to 0.864 with AI assistance (odds ratio [OR], 3.71; P<0.001). In randomized Phase 4, AI assistance improved accuracy vs reference-sheet support alone (OR, 4.28; 95% CI, 3.08-5.95; P<0.001), and Grad-CAM provided additional benefit (OR, 1.53; 95% CI, 1.04-2.25; P=0.033). Exploratory analyses showed greater improvement among less experienced readers; with AI assistance, non-cardiologists outperformed cardiologists. AI-assisted readers remained less accurate overall than the model alone. Conclusions: An ECG image-based ensemble differentiated Takotsubo syndrome from anterior STEMI and improved reader accuracy. Visual explanations provided additional benefit, while experience-related differences highlight the importance of effective human-AI interaction.

Study Objectives: Glucagon-like peptide-1 (GLP-1) receptor agonists raise resting heart rate. We asked whether, after semaglutide initiation, the increase is larger during rapid eye movement (REM) sleep than during non-rapid eye movement (NREM) sleep. Methods: In the Human Phenotype Project, an Israeli cohort of generally healthy adults aged 40 to 70 years, 96 of 494 self-reported semaglutide initiators had a home sleep study (WatchPAT; sleep stages estimated from peripheral arterial tone and actigraphy) before and at least three months after starting. Each was propensity-matched 1:1 to a never-user of GLP-1 drugs measured at the same visits. We estimated the initiators' change minus their controls' change (difference-in-differences). The REM minus NREM contrast was designated primary after an exploratory analysis. Results: Relative to controls (90 to 94 pairs), heart rate rose during nocturnal wake (+3.89 beats per minute [bpm], 95% confidence interval 2.35 to 5.44), NREM sleep (+4.82 bpm, 3.12 to 6.52) and REM sleep (+6.25 bpm, 4.42 to 7.91); the REM increase exceeded the NREM increase by 1.42 bpm (0.98 to 1.89). The excess persisted across matching designs and on a relative scale; the proportion of REM sleep did not change. Never-users with similar weight loss showed no REM sleep heart-rate rise (+0.40 bpm, -0.63 to 1.50). Heart-rate variability decreases disappeared after adjustment for heart rate. Conclusions: Semaglutide initiation was associated with higher heart rate throughout sleep, most during REM sleep, unexplained by weight loss. Clinic measurements may understate this effect. Confirmation with polysomnographic staging is needed.

Background: The systemic immune-inflammation index (SII) is a routinely available blood-derived marker with potential prognostic relevance after coronary artery bypass grafting (CABG). We evaluated associations between preoperative SII and postoperative outcomes. Methods: PubMed, Embase, Scopus and Web of Science were searched to 1 October 2026, with supplementary searches through 14 October 2026. Peer-reviewed observational studies evaluating preoperative SII in adults undergoing CABG were eligible. Methodological quality was assessed using the Newcastle-Ottawa Scale. Compatible categorical associations and exploratory adjusted continuous mortality effects were pooled separately using restricted maximum-likelihood random-effects models; other findings were summarized descriptively. Results: Thirty-one reports were included, with an aggregate sample of 30,976 and possible cohort overlap; five contributed to meta-analysis. Two off-pump cohorts comprising 1,378 patients and 180 atrial-fibrillation events yielded an imprecise crude association with postoperative atrial fibrillation (OR 1.60, 95%CI 0.53-4.82; I2=68%). An exploratory crude synthesis indicated greater intra-aortic balloon pump use with higher SII (OR 2.45, 95%CI 1.16-5.18; I2=51%). Two cohorts with differing follow-up yielded an exploratory adjusted mortality HR of 1.11 (95% Wald CI 1.05-1.18) per study-specific SD increase in log-SII. In one 624-patient cohort, high SII was associated with postoperative acute kidney injury after adjustment (OR 3.35, 95% CI 2.25-4.97).Apparent atrial-fibrillation discrimination varied (AUC 0.587-0.946). Conclusions: Preoperative SII showed associations with selected adverse outcomes after CABG, supporting further evaluation as an accessible prognostic marker. Its incremental value beyond established clinical risk models requires validation. Keywords: Systemic immune-inflammation index; Coronary artery bypass grafting; Atrial fibrillation; Prognosis; Systematic review

Purpose: To compare the performance of three large language models (LLMs), ChatGPT-o1, Grok 3, and Gemini 2.0 Pro, on the American College of Prosthodontists' National Prosthodontic Resident Examination (NPRE). Materials and Methods: Official NPRE examinations from years 2015-2024 were retrieved from the American College of Prosthodontists website and used to prompt each LLM. Paid access tiers and advanced reasoning were enabled. Responses from LLMs were recorded and compared against the official answer keys for accuracy, variability across years, and consistency of responses. Duplicate questions were retained for the primary analysis and removed in a sensitivity analysis. Accuracy (percent correct) was compared using generalized estimating equations binary logistic regression (primary); Cochran's Q with Bonferroni-adjusted McNemar tests (sensitivity). Year-to-year variability (SD of annual accuracy) was compared using weighted Levene tests. Consistency across duplicate items was quantified as the percentage of perfect matches and compared using Cochran's Q with Bonferroni-adjusted McNemar tests. Results: Across 1,487 questions, overall accuracy differed among models (global P < 0.001). ChatGPT-o1 achieved 78.4% correct, Grok 3 76.4%, and Gemini 2.0 Pro 73.2%; ChatGPT-o1 and Grok 3's accuracy significantly exceeded Gemini 2.0 Pro (both P [&le;] 0.016), with no significant difference between ChatGPT-o1 and Grok 3 (P = 0.099). After removing duplicates (n = 1,200), results were similar: ChatGPT-o1, Grok 3, and Gemini 2.0 Pro were 77.1%, 76.6%, and 71.5% correct, respectively; ChatGPT-o1 and Grok 3's accuracy was significantly higher than Gemini 2.0 Pro (both P < 0.001). Variability across years did not differ significantly (P [&ge;] 0.097). Grok 3 showed significantly higher perfect-match consistency (79.2%) than ChatGPT-o1 (69.4%) and Gemini 2.0 Pro (64.5%) (P [&le;] 0.010). Conclusions: Contemporary LLMs showed 70-80% accuracy on postgraduate prosthodontic multiple-choice content. While promising for educational support, residual error and inconsistency warrant human oversight and transparent methods reporting before curricular adoption.

Background: Ambient artificial-intelligence (AI) scribes are being deployed beyond medicine into allied-health practice, but comparative evidence mainly concerns physician documentation. Objective: To compare quality and transcript-grounded clinical-risk error burden of AI-generated and allied health professional notes from identical simulated consultations. Methods: Paired simulation across dietetics, physiotherapy, occupational therapy and speech and language therapy (five consultations each; 20 pairs; 40 notes). Heidi generated an unedited first-draft note and a professional documented each encounter. Professionals rated PDQI-9 quality. Professional and automated review identified unsupported statements and omissions; two language models graded clinical risk. Co-primary outcomes were PDQI-9 score and Critical+High error density. Results: Mean PDQI-9 scores were 38.45 and 35.05 for AI and professional notes (paired difference +3.40, 95% CI +0.01 to +6.79; Wilcoxon p=0.042; dz=0.47). The Bayesian estimate was +3.47 (94% equal-tailed credible interval +0.66 to +6.30; P[AI>professional]=0.989). Automated-reviewer all-error density was 3.35 vs 8.55 per note (professional-minus-AI difference 5.20, 95% CI 3.85 to 6.55); omission density was 1.45 vs 5.20 (difference 3.75, 2.80 to 4.65). Critical+High density was 0.35 vs 0.75 (difference 0.40, -0.10 to 1.00). The pooled quality result was driven principally by dietetics. Conclusions: AI-generated notes achieved modestly higher pooled quality and fewer transcript-grounded errors, chiefly omissions; serious-error evidence was inconclusive. Small discipline samples and incomplete ground truth preclude safety or discipline-wide superiority claims.

The aim of this study was to develop a framework for assessing the incremental value of decisions on drug amount, timing, input structure, cessation and restart. The LOGOS (Longitudinal Optimization of Global Outcomes and Strategies) approach combines matched nested regimen classes, mechanism-specific benchmarks and evidence requirements in a single longitudinal comparison. Thus, the contribution of additional regimen freedom is assessed separately from improvements due to different resources, constraints or evaluation horizons. Nested feasible classes have nondecreasing objective suprema, whereas a strict gain requires a feasible witness above a valid bound for the smaller class. The approach is illustrated through analytical examples of periodic smoothing, matched desensitization, relaxed exposure allocation, finite-course loading and safety after cessation. In a normalized finite Emax model, integrated benefit increased from 0.629606 for three equal boluses to 0.672945 after optimization of amounts, 0.677043 for an amount-and-time candidate and 0.679553 for a globally certified loading-maintenance-coast solution. The adjacent observed gains were 6.88%, 0.61% and 0.37%, respectively, and all schedules satisfied the full-tail injury bound. In addition, a periodic factorial example separated response curvature from a joint recovery and repair process, including a null case and results dependent on feasibility constraints. The LOGOS framework allows assessment of whether additional regimen flexibility improves benefit, provides no added value or restores feasibility. These dimensionless examples support conditional mathematical and model-based conclusions; clinical superiority and safe restart in patients have not been established.