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ImportanceHospital ratings including the US News & World Report’s Best Hospitals rankings and the Centers for Medicare & Medicaid Services’ (CMS’) Overall Hospital Quality Star Rating (Overall Star Rating) measure different outcomes and are weakly correlated. Therefore, methods for defining and measuring reliable excellence, defined as consistently great performance across all quality measures, are needed.ObjectiveTo assess a measure of reliable excellence using the 45 quality measures reported in the Overall Star Ratings.Design, Setting, and ParticipantsThis cross-sectional study used hospital-level data from the 2023 and 2024 CMS Overall Star Ratings at all US hospitals with a 2023 and 2024 CMS Overall Star Rating.ExposuresThe exposure was the CMS Overall Star Rating summary score, a continuous variable calculated from the weighted z scores of 45 quality measures used in the Overall Star Rating. A total of 100 000 simulations were run in which all US hospitals’ CMS Overall Star Rating summary scores were calculated through summation of z scores from randomly generated measure weights, as opposed to the existing weights used in the Overall Star Rating method.Main Outcomes and MeasuresReliable excellence, defined as achieving a 90th percentile (or better) CMS Overall Star Rating summary score on at least 50 000 of 100 000 simulations. The percentage of hospitals achieving reliable excellence was calculated both overall and stratified by CMS Ovearll Star Ratings.ResultsThere were 2700 hospitals in the analysis, with 335 5-star hospitals (12.4%), 727 4-star hospitals (26.9%), 799 3-star hospitals (29.6%), 572 2-star hospitals (21.2%), and 267 1-star hospitals (9.9%) in the 2024 CMS Overall Star Rating. A total of 244 of 2700 hospitals (9.0%) met the study definition of reliable excellence, whereas 1287 of 2700 hospitals (47.7%) achieved excellence in at least 1 simulation.Conclusions and RelevanceThis cross-sectional study of 2700 US hospitals found that only 244 hospitals (9.0%), including less than two-thirds of the CMS 5-star rated hospitals, were reliably excellent across 100 000 CMS Overall Star Rating scoring simulations using random measure weightings. These findings lend credence to the ubiquity of inconsistent greatness in health care quality and illuminate the need for methods to distinguish hospitals that provide reliably excellent care.

WITHDRAWN

OBJECTIVE Idiopathic normal pressure hydrocephalus (iNPH) is characterized by gait deterioration, cognitive disturbances, and urinary incontinence. Gait deficits pose safety hazards due to frequent falls, yet objective investigations of gait improvement following ventriculoperitoneal shunt (VPS) placement are limited. Therefore, the aim of this study was to assess objective improvement in gait parameters for patients with iNPH after VPS placement. METHODS All patients treated with a VPS for iNPH by the senior author at a single tertiary care center from December 2017 to January 2023 were retrospectively reviewed. Patients who underwent detailed motion analysis testing before surgery with > 1 follow-up assessment were included. Analyzed parameters included gait velocity, gait cadence, stride length, stride width, gait stability ratio, total support, and single support. Time-dependent linear mixed-effects models and generalized additive mixed-effects models (GAMMs) were used to model short-term and longitudinal gait parameter changes, respectively. RESULTS A total of 212 patients (137 male, median age was 75.2 years) who underwent VPS placement for iNPH were included, with a total of 747 gait analyses performed at a median last-gait analysis follow-up duration of 12.0 months. Statistically significant changes in all gait parameter values were seen between the preoperative time point and first postoperative follow-up (within 3 months) time point (paired Wilcoxon p < 0.001), with a statistically significant majority of patients experiencing improvement (binominal p < 0.0001). Longitudinal analyses using a GAMM showed increased gait velocity, single support, and stride length, with decreased stability ratio, step width, and total support at early time points, while a plateau was achieved around 6 months. CONCLUSIONS A detailed gait analysis demonstrated significant improvements in objective ambulation metrics, particularly in the gait velocity, stride length, and gait stability ratio, following VPS placement in patients with iNPH. Most patients maintained marked improvement from baseline to the last follow-up assessment, although a plateau of the benefit was experienced over longer follow-up.

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

ABSTRACT Bipolar disorder’s (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder—and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic–GABAergic gradient along the psychotic factor. BD’s genetic architecture appears hierarchical—a general liability resolving into dimensions of course and comorbidity beyond diagnostic subtypes.

Introduction Differentiated thyroid cancer (DTC) is the most common endocrine malignancy, with a high 5-year survival rate of approximately 98%. Despite advances in diagnosis and treatment, up to 20% of patients experience recurrence, adversely affecting their quality of life. Predictive models have been developed to assess recurrence risk and guide clinical decision-making, but these models often face limitations such as retrospective design, lack of diversity in study populations and absence of external validation. The primary aim is to externally validate existing predictive models for DTC recurrence using prospective data from a diverse Latin American cohort. The secondary aim is to explore opportunities for model recalibration to improve their performance in our population. Methods and analysis The CaTaLiNA study is a multicentre prospective observational study conducted across 10 hospitals in five Latin American countries, including Ecuador, Peru, Uruguay and Mexico. Patients aged 18 years or older receiving treatment for DTC, such as the first thyroid surgery, active surveillance or radiofrequency ablation will be included. Recruitment will occur from November 2023 to June 2025, with follow-up extending until June 2028. Data collection will include baseline clinical, surgical and histological characteristics, treatment details and follow-up outcomes. Statistical analysis will follow the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines, using imputation strategies for missing data and evaluating calibration and discrimination of the prediction models. Calibration measures include the ratio of expected and observed events, calibration slope and calibration plot, while discrimination will be assessed using the C-index and area under the receiver operating characteristic curve. Ethics and dissemination This study protocol was approved by Comité de Ética de Investigación en Seres Humanos de la Universidad San Francisco de Quito USFQ ‘CEISH-USFQ’ APO-010–2023-CEIHS-USFQ Oficio No. 161-2023-CA-23030M-CEISH-USFQ. Results will be disseminated via peer-reviewed publications.

ABSTRACT Background Membranous nephropathy (MN) associated with phospholipase A2 receptor (PLA2R) antibodies is the most common cause of nephrotic syndrome in non-diabetic adult patients. This study investigated the relationship between clinical phenotypes and renal outcomes in this population, emphasizing the potential for phenotype-based treatment stratification. Methods We conducted a retrospective, multi-center cohort study of PLA2R-positive MN. Unsupervised cluster analysis grouped patients based on clinicopathological characteristics. Primary outcomes included complete or partial remission within 2 years of biopsy and end-stage kidney disease (ESKD) or death during follow-up. Results Among 178 patients, three distinct clusters emerged (n = 89, 70, and 19). Within 2 years of biopsy, 102 patients (57%) achieved complete or partial remission. Cluster 1, characterized by the mildest disease markers, including the lowest body mass index, serum anti-PLA2R titer, serum creatinine, proteinuria, and triglycerides, had the highest remission rate of 72%, compared with 50% in cluster 2 and 54% in cluster 3. Cluster 2 had significantly lower remission compared to cluster 1 (HR 0.64, 95% CI 0.42–0.96, P = .03); results were similar in cluster 3, albeit not statistically significant (HR 0.50, 95% CI 0.23–1.09, P = .08). Elevated anti-PLA2R levels (&gt;100 U/ml) and proteinuria (&gt;8 g/24-hour) predicted reduced remission (HR 0.56, 95% CI 0.36–0.88, P = .01; HR 0.43, 95% CI 0.26–0.73, P = .002, respectively), while high high-density lipoprotein levels were protective (HR 1.01, 95% CI 1.00–1.02, P = .048). Overall, 21 patients (12%) developed ESKD or died. ESKD-free survival differed significantly across clusters (log-rank test P = .04). Conclusions Unsupervised clustering identified distinct clinical phenotypes in PLA2R-positive MN, each associated with different renal prognoses. Phenotype-based risk stratification could enhance treatment precision, improve patient outcomes, and potentially reduce treatment-related adverse effects.