Evidence map›Paper›PMID 40459891›Full record

ArticleJAMA network open2025

Identifying Inpatient Pediatric Services Across National Datasets.

Corrie E McDaniel, Mark Ralston Daniel, Seneca D Freyleue, Edouard Seryozhenkov, Amit Peled, Harsha Amaravadi, Niharika Malla, JoAnna K Leyenaar

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Corrie E McDanielDepartment of Pediatrics, Division of Hospital Medicine, University of Washington, Seattle.
Mark Ralston DanielData Science, University of Washington; Seattle.
Seneca D FreyleueThe Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Edouard SeryozhenkovData Science, University of Washington; Seattle.
Amit PeledData Science, University of Washington; Seattle.
Harsha AmaravadiSchool of Public Health, University of Washington, Seattle.
Niharika MallaSchool of Public Health, University of Washington, Seattle.
JoAnna K LeyenaarThe Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.

Funding

Urban-Rural Disparities in Healthcare Quality for Children with Complex or Disabling Health ConditionsR01MD014735 · NIMHD · DARTMOUTH-HITCHCOCK CLINIC · PI LEYENAAR, JOANNA · 2020 to 2024
$3.0M
AHRQ HHS K08 HS028683NIMHD NIH HHS R01 MD014735
6 · The paper itself

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.

Indexed as

Hospitals, PediatricInpatientsPediatricsChildChild, PreschoolCross-Sectional StudiesDatasets as TopicHumansInfantInfant, NewbornUnited States

Identifiers

PMID40459891
PMCPMC12134956

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.