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Hepatic resection (HR) and liver transplantation (LT) are curative-intent treatments for hepatocellular carcinoma (HCC). Patients without cirrhosis are typically analyzed as a single group, obscuring potentially important differences between those with histologically normal liver and those with non-cirrhotic parenchymal disease. We aimed to characterize long-term outcomes following HR stratified by underlying parenchymal quality, with a contemporary LT cohort as an external benchmark. Retrospective cohort study of 662 adults undergoing curative-intent surgery for HCC at 2 tertiary centers (2010–2024). Resection patients (n=380) were stratified by non-tumor parenchymal histology into HR-Normal (n=84), HR-Diseased non-cirrhotic (n=159), and HR-Cirrhotic (n=137). A total of 282 LT recipients served as an external reference but were not entered into comparative models. Overall survival (OS) was analyzed by Kaplan–Meier and multivariable Cox regression, and recurrence by Fine–Gray competing-risks regression. Ninety-day mortality was low across resection strata (1.2%–3.2%). OS differed significantly across parenchymal groups (log-rank p =0.01), driven by superior outcomes in HR-Normal patients compared with both HR-Cirrhotic ( p =0.008) and HR-Diseased ( p =0.005). HR-Cirrhotic and HR-Diseased did not differ ( p =0.97). Five-year OS was 74.4%, 59.8% and 57.2%, respectively. On multivariable Cox regression, both cirrhosis (HR 2.03, 95% CI 1.16–3.54, p =0.01) and diseased non-cirrhotic parenchyma (HR 2.04, 95% CI 1.21–3.43, p =0.008) independently predicted death versus normal liver. Fine–Gray analysis confirmed higher recurrence risk in cirrhotic (sHR 2.09, p =0.01) and diseased non-cirrhotic (sHR 1.80, p =0.04) groups. The LT benchmark cohort achieved a 5-year OS of 82.4%. Histologically normal liver identifies a distinct HR subgroup with favorable oncologic outcomes. The conventional cirrhotic/non-cirrhotic dichotomy underestimates the prognostic impact of underlying parenchymal disease.

BACKGROUND: Surgeons and ostomy nurses receive a high volume of stoma photos from patients. OBJECTIVE: This study aimed to develop and validate an automated and scalable artificial intelligence pipeline for identification and triage of patient-submitted intestinal ostomy photos. DESIGN: Retrospective derivation and validation study with stakeholder engagement to guide model design. Clinical teams reviewed photos in duplicate and categorized them into: (a) obstructed view, (b) healthy stoma, (c) suitable for conservative management, or (d) requiring in-person review. Pre-trained neural networks including MobileNetV4, ResNet50, ViT and CLIP-ViT were fine-tuned with 5-fold cross-validation. SETTINGS: Nine Mayo Clinic hospitals (2019–2022). PATIENTS: Adult patients (≥18 years) undergoing surgery who submitted images within 30 days after surgery. MAIN OUTCOME MEASURES: Model performance was evaluated with area under the receiver operator curve, precision, recall, and F1-scores. RESULTS: In this study, 538 photos of abdominal ostomies were sent in by 191 patients (median age 52 years, IQR 40–63; 60.4% female). Expert consensus triaged 236 (43.9%) photos as having an obstructed view, 30 (5.6%) as healthy, 177 (32.9%) for conservative management, and 95 (17.7%) for in-person review. All models achieved area under the receiver operator curve >0.97 for identifying stomas. The CLIP-ViT model performed best at triage (macro- area under the receiver operator curve 0.94 ± 0.06; F1 0.77 ± 0.10). End-to-end detection and triage using CLIP-ViT achieved area under the receiver operator curve of 0.99 ± 0.01, precision of 0.87 ± 0.08, recall of 0.93 ± 0.04, and F1 of 0.89 ± 0.07. Attention maps showed that models focused on stomas to determine classification. LIMITATIONS: Low sample size and lack of prospective validation. CONCLUSIONS: A vision language model pipeline accurately detected and triaged patient-submitted ostomy photos. Prospective evaluation is now needed to support integration into multidisciplinary digital workflows. See Video Abstract .

Background Hypertensive disorders of pregnancy (HDP) are a leading global cause of maternal and fetal morbidity/mortality. Most estimates of HDP occurrence are reported for composite exposures of HDP or preeclampsia alone, with limited disaggregation by obstetrical subgroup (eg, multi‐fetal deliveries). Consequently, our understanding of HDP prevalence and temporal trends remains limited, and methods for identifying specific diagnoses in administrative data remain underexamined. This population‐based descriptive study assessed how enhanced administrative case‐finding definitions influence prevalence estimates of HDP in pregnancy and postpartum. Methods Included were individuals aged ≥18 years discharged from an Ontario hospital following their first obstetrical delivery between April 1, 2002, and December 31, 2017. We calculated overall and annual prevalence estimates and assessed temporal trends for any HDP and major subtypes by case‐finding definitions, stratified by plurality (singleton versus multi‐fetal deliveries). Results Most subjects had a singleton first delivery (n=781 258; 98%); however, HDP was 2.2 to 2.4 times more prevalent in multi‐fetal deliveries. When pre‐pregnancy hypertension was considered during case‐finding, HDP prevalence increased from 193.6 to 207.6 per 1000 multi‐fetal deliveries and from 79.9 to 92.6 per 1000 singleton deliveries. Gestational hypertension was the most common diagnosis in both populations but was nearly twice as common in those who delivered a multiple gestation (94.4–100.0 versus 49.7–53.4 per 1000 deliveries). Prevalence trends differed by coding strategy and plurality. Conclusions Although informative, data from discharge abstracts alone are insufficient for HDP surveillance, particularly for chronic hypertension in pregnancy. Validation studies should assess the accuracy of our case‐finding definitions against suitable reference standards.

Objective We aimed to use machine learning (ML) models to investigate the impact of clinical, social and behavioural factors on 1-year progression from pre-diabetes to type 2 diabetes mellitus (DM). Design A retrospective cohort study. Setting A large health system including eight sites in southeast Michigan. Participants Adults with haemoglobin A1c (HbA1c) between 5.7% and 6.4% for two consecutive years between 1 January 2008 and 31 December 2023, and no prior history of type 2 DM or metformin use. Primary outcome measure New-onset type 2 DM (HbA1c ≥6.5%) in 1 year. Results Among 11 809 individuals, 815 (6.9%) progressed to type 2 DM within 1 year. CatBoost demonstrated the best performance (average area under the curve 0.78). Prior-year HbA1c was the most influential covariate (SHapley Additive exPlanations 1.02). Traditional metabolic factors (high-density lipoprotein, body mass index (BMI), white blood cell, age, gender, triglycerides) also contributed. Lastly, while inclusion of social and behavioural determinants of health (SBDH) did not significantly improve the overall model performance, depression emerged as the prominent SBDH covariate. Depression was a stronger predictor of diabetes progression in individuals with higher baseline BMI and lower baseline HbA1c. Conclusions Inclusion of social and behavioural covariates provided no incremental value for prediction of diabetes progression from pre-diabetes. However, machine learning revealed that depression may play a role in progression to type 2 DM.

Abstract Background Failure to rescue (FTR) is a key driver of postoperative mortality and reflects a hospital’s ability to recognize, escalate, and treat complications. Although FTR varies substantially between hospitals, the hospital characteristics that underpin this variation are inconsistently described. The aim of this study was to synthesize evidence on hospital-level factors associated with FTR after postoperative complications across surgical populations and health systems. Methods A systematic review of observational studies evaluating associations between hospital-level characteristics and FTR in surgical patients was undertaken. MEDLINE, EMBASE, and Scopus were systematically searched on 17 July 2024 and updated to 31 December 2025. Hospital factors were classified into structural, staffing, and system domains. Where three or more non-overlapping studies reported adjusted estimates in comparable formats, random-effects meta-analyses were performed; otherwise, findings were summarized narratively. Methodological quality of included studies was assessed using the ROBINS-I tool (version 2). Results A total of 111 studies encompassing over 137 million patients and 301 hospital factors were included. Meta-analyses demonstrated lower FTR rates in larger hospitals (odds ratio 0.85, 95% confidence interval 0.74 to 0.97), hospitals with greater intensive care unit capacity (odds ratio 0.80, 0.71 to 0.90), and teaching hospitals (odds ratio 0.88, 0.83 to 0.93). Better nursing resources were consistently associated with lower FTR, including patient-to-nurse ratios (odds ratio 1.07, 1.03 to 1.10), higher nurse education (odds ratio 0.95, 0.91 to 0.99), and more favourable work environments (odds ratio 0.93, 0.90 to 0.95). Safety-net hospital status was associated with higher FTR (odds ratio 1.22, 1.03 to 1.45). Substantial heterogeneity was observed across most analyses. Conclusion Hospital-level structural, staffing, and system characteristics are associated with variation in FTR. Postoperative mortality is closely linked to institutional capacity to detect and manage complications, underscoring the importance of adequate resourcing and system organization to improve surgical outcomes.

Context.— One objective of the College of American Pathologists/American College of Medical Genetics Cytogenetics Committee is to ensure the accurate detection and clinical interpretation of abnormalities observed by fluorescence in situ hybridization (FISH) analysis when evaluating hematologic neoplasms. Objective.— To report a 20-year performance summary (2005–2024) of FISH challenges focusing on hematologic neoplasms. Design.— A retrospective review was performed from 2005 through 2024 to identify FISH challenges specifically addressing hematologic neoplasms. The overall performance of participants was evaluated to identify potential recurring errors of clinical relevance. Results.— A total of 124 hematologic neoplasm FISH challenges from 2005 to 2024 were provided to participating laboratories, including 45 paper challenges (results and/or images were provided for interpretation purposes), 43 liquid challenges (bone marrow aspirate or peripheral blood), and 36 formalin-fixed paraffin-embedded (FFPE) tissue challenges. Of the 124 challenges, 120 (97%) exceeded the required 80% consensus for grading purposes. Of the 4 failed challenges (3%), 2 (1 liquid, 1 paper) involved the interpretation of atypical FISH signal patterns; 1 (paper) involved the interpretation of FISH signal patterns correlating involvement by myeloid versus lymphoid interphase nuclei; and 1 (liquid) likely involved a specimen processing or handling error that resulted in discordant participant responses. All 36 FFPE tissue challenges exceeded the 80% consensus for grading purposes. Conclusions.— This 20-year retrospective review demonstrates that clinical cytogenomic laboratories have been and continue to be highly proficient in the detection and interpretation of FISH abnormalities associated with hematologic neoplasms.

Abstract Frontotemporal dementia (FTD) is characterized by selective neuronal vulnerability, yet the features that predispose specific neuron types to degeneration remain unclear. We performed single-nucleus RNA sequencing of frontoinsular cortex, a region affected early in behavioral variant FTD, across individuals with C9orf72 -associated and sporadic FTD-MND spectrum disease. By enriching for large projection neurons, we resolved molecular subtypes of layer 5 extratelencephalic neurons, including von Economo neurons, and identified selective depletion of specific layer 2/3 and layer 5 neuron subtypes, convergent across genotypes. Despite selective neuronal loss, disease-associated transcriptional changes were convergent across excitatory neuron populations, suggesting that they reflect upstream pathophysiology or shared responses to local neurodegeneration. By relating neighborhood-level depletion in disease to gene expression in controls, we found that baseline cellular respiration and ATP synthesis predict neuronal vulnerability in disease. These findings define molecular correlates of selective neuronal vulnerability in FTD and provide a framework linking cell type and state to neurodegeneration.

ABSTRACT Approximately 30% of patients with acute graft-versus-host disease (GVHD) develop steroid-refractory disease and have very poor outcomes. Ruxolitinib has become the standard of care for steroid-refractory acute GVHD, but it is unclear which patients derive benefit. The MAGIC Composite Score (MCS), an algorithm that combines clinical symptoms and biomarkers, has been validated to predict outcomes at the start of primary GVHD treatment. Here, we evaluated its performance at the initiation of second-line treatment in 278 patients. MCS stratified patients into three risk groups (MCS1-3), with the majority (88%) classified as intermediate or high risk. Increasing MCS score was associated with progressively higher 1-year non-relapse mortality (NRM) rates (16%, 41%, and 73%; p<0.001), lower 1-year survival (77%, 56%, and 24%; p<0.001), and lower complete response (CR) rates at day 28 (47%, 38%, and 20%, respectively; p<0.01). The area under the receiver operating characteristic curve (AUROC) for 1-year NRM was significantly higher with MCS compared to clinical symptoms alone (0.70 vs. 0.63; p=0.023). Among patients treated with ruxolitinib, higher MCS similarly predicted higher NRM and lower survival and CR rates. Patients classified as MCS2/3 had poor outcomes despite ruxolitinib, underscoring the need for novel therapies in this patient population. In conclusion the MCS is an accurate predictor of outcomes for patients who require second-line treatment and may be of use as an eligibility criterion for future clinical trials in this high-risk population. Key Points MAGIC Composite Scores that integrate clinical symptoms and biomarker scores predict outcomes of second-line therapy for acute GVHD more accurately than clinical symptoms alone. MCS identifies patients with high NRM despite treatment with ruxolitinib.

Most patients with chest pain do not have acute myocardial infarction (AMI), yet post-AMI rule-out disposition remains variable. Detectable but sub-99th percentile high-sensitivity cardiac troponin I (hs-cTnI) provides additional prognostic information. We evaluated the economic value of hs-cTnI-guided hospitalizations among non-AMI patients. We analyzed 1481 non-AMI chest pain visits with detectable baseline hs-cTnI across 29 U.S. emergency departments (2014–2016) in the prospective HIGH-US trial using the Atellica IM TnIH assay(Siemens Healthcare Diagnostics, Tarrytown, NY, US). We compared observed standard-of-care admissions with modeled pathways incorporating hs-cTnI thresholds and risk scores, assessing admission rates, costs, and diagnostic accuracy for 30-day death/MI. Overall, 1.0% (n = 15/1481) experienced 30-day death/MI. Standard-of-care admitted 59% of patients with 93% sensitivity and 41% specificity. The modeled pathway using hs-cTnI ≥ 5 ng/L plus non-low risk scores was associated with reduced projected admission of 41%, higher specificity of 60%, and the same 93% sensitivity. Estimated per-patient diagnostic costs were lower under Scenario 1 (noninvasive diagnostic testing only; $1025 vs. $1139) and under Scenario 2 (including inpatient/invasive procedures; $2672 vs. $3536). In subgroups, higher hs-cTnI thresholds conferred further economic benefit without compromising sensitivity. Incorporating sub-99th percentile hs-cTnI thresholds alongside risk scores may support more efficient resource use while maintaining safety, although findings require prospective validation.

Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.