ArticleJAMA network open2025
Identifying Inpatient Pediatric Services Across National Datasets.
Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Pediatric Capabilities in US Hospitals: 2003-2022.Pediatrics · 2026Article
- Development of an Atlas for US Pediatric Acute Care.JAMA pediatrics · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
Importance: National statistics about regionalization and access to hospitals' pediatric services have been derived from different datasets with differing sampling frames, sizes, and designs, generating conflicting estimates about pediatric service accessibility. Objective: To calculate test characteristics for the provision of pediatric hospital-based inpatient services in 3 national datasets and explore models for improving service identification in a merged dataset. Design, Setting, and Participants: This cross-sectional study analyzed pediatric services in 3114 US hospitals common across the American Hospital Association Annual Survey (AHA), Centers for Medicare & Medicaid Services Provider of Service File (POS), and National Pediatric Readiness Project (NPRP) in 2021. Analysis was conducted June 2024 to March 2025. Exposure: Provision of 4 pediatric services-newborn, neonatal intensive care, general pediatric inpatient care, and pediatric intensive care. Main Outcomes and Measures: Test characteristics and model performance were calculated and reported as F1 scores, a machine learning evaluation metric that calculates the harmonic mean of precision and recall within a model, for the provision of services as reported in the AHA and POS relative to the NPRP, this study's benchmark for pediatric service reporting. Logistic regression, random forest, gradient-boosted trees, and rule-based models were tested to estimate pediatric service provision using a merged dataset. Results: Of 3114 hospitals, NPRP identified 2742 providing newborn care (88.1%), 1375 with neonatal intensive care (44.2%), 2204 offering general pediatric care (70.8%), and 450 with pediatric intensive care (14.5%). For newborn care, AHA data showed 95.7% agreement with NPRP (F1 = 0.97; 95% CI, 0.96-0.97), while POS showed 89.4% (F1 = 0.62; 95% CI, 0.60-0.64). For neonatal intensive care, agreement was 89.8% for AHA (F1 = 0.86; 95% CI, 0.85-0.88) and 72.9% for POS (F1 = 0.75; 95% CI, 0.74-0.77). General pediatric care showed lower agreement, with AHA showing 65.6% agreement (F1 = 0.69; 95% CI, 0.67-0.71) and POS showing 69.7% agreement (F1 = 0.79; 95% CI, 0.77-0.80). For pediatric intensive care, AHA agreement was 81.5% (F1 = 0.91; 95% CI, 0.90-0.93) while POS was 78.3% (F1 = 0.49; 95% CI, 0.46-0.51). Merging datasets modestly improved service identification accuracy. Conclusions and Relevance: In this cross-sectional study of commonly used datasets, reporting of pediatric service provision varied significantly. As these datasets inform pediatric health care policy, these results may guide approaches to optimize service line definitions.
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