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Record W2781993063 · doi:10.1177/0844562117751313

Relationships Among Neonatal Mortality, Hospital Volume, Weekday Demand, and Weekend Birth

2018· article· en· W2781993063 on OpenAlexvenueno aff
Elizabeth Restrepo, Patricia Hamilton, Fuqin Liu, Peggy Mancuso

Bibliographic record

VenueCanadian Journal of Nursing Research · 2018
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsBirth certificateMedicineLogistic regressionDemographyOddsPopulationDeath certificateCause of deathEnvironmental healthDisease

Abstract

fetched live from OpenAlex

Background Research findings indicate that hospital volume affects the quality of care, that quality and demand for care on weekends differs from weekdays, and that giving birth on the weekend increases odds of neonatal mortality. Purpose To explore relationships among neonatal mortality, hospital volume, weekday demand for services, and weekend birth and risk of neonatal mortality. Methods For this retrospective, population-based, cohort study design, data were obtained from 32,140 electronic birth certificate records matched with 92 death certificate records from the Texas Department of State Health Services for 2012. Statistical analyses include descriptive procedures, analysis of variance, bivariate correlation, t-test, logistic regression, and chi-square tests of association. Results Higher hospital birth volume and higher concentrations of births during the week were associated with fewer neonatal deaths. Weekend births were associated not only with higher rates of neonatal death but also with lower birth weight and ethnicity of the mother. Conclusions These findings suggest the need for further study of the ways hospital-level organization of services and resources interact with individual risk factors to play a significant role in raising the neonatal mortality risk associated with weekend birth.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.092
GPT teacher head0.382
Teacher spread0.290 · 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 teacher head, 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

Citations9
Published2018
Admission routes1
Has abstractyes

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