MétaCan
Menu
Back to cohort
Record W4362523686 · doi:10.1542/hpeds.2022-006931

Characterization of Birth Hospitalizations in the United States

2023· article· en· W4362523686 on OpenAlexaff
Lucky Ding, Jonathan Rodean, JoAnna K. Leyenaar, Eric R. Coon, Sanjay Mahant, Peter J. Gill, Michael D. Cabana, Sunitha V. Kaiser

Bibliographic record

VenueHospital Pediatrics · 2023
Typearticle
Languageen
FieldMedicine
TopicNeonatal Health and Biochemistry
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicinePediatricsPregnancyDemographicsJaundiceDemography

Abstract

fetched live from OpenAlex

OBJECTIVES: A broad understanding of the scope of birth hospitalizations in the United States is lacking. We aimed to describe the demographics and location of birth hospitalizations in the United States and rank the most common and costly conditions documented during birth hospitalizations. METHODS: We conducted a cross-sectional analysis of the 2019 Kids' Inpatient Database, a nationally-representative administrative database of pediatric discharges. All hospitalizations with the indicator "in-hospital birth" and any categorized by the Pediatric Clinical Classification System as "liveborn" were included. Discharge-level survey weights were used to generate nationally-representative estimates. Primary and secondary conditions coded during birth hospitalizations were categorized using the Pediatric Clinical Classification System, rank-ordered by total prevalence and total marginal costs (calculated using design-adjusted lognormal regression). RESULTS: In 2019, there were an estimated 5 299 557 pediatric hospitalizations in the US and 67% (n = 3 551 253) were for births, totaling $18.1 billion in cost. Most occurred in private, nonprofit hospitals (n = 2 646 685; 74.5%). Prevalent conditions associated with birth admissions included specified conditions originating in the perinatal period (eg, pregnancy complications, complex births) (n = 1 021 099; 28.8%), neonatal hyperbilirubinemia (n = 540 112; 15.2%), screening or risk for infectious disease (n = 417 421; 11.8%), and preterm newborn (n = 314 288; 8.9%). Conditions with the highest total marginal costs included specified conditions originating in perinatal period ($168.7 million) and neonatal jaundice with preterm delivery ($136.1 million). CONCLUSIONS: Our study details common and costly areas of focus for future quality improvement and research efforts to improve care during term and preterm infant birth hospitalizations. These include hyperbilirubinemia, infectious disease screening, and perinatal complications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.265
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueHospital PediatricsSame topicNeonatal Health and BiochemistryFrench-language works237,207