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Record W3114888628 · doi:10.1515/jpm-2020-0133

Racial disparities in recurrent preterm delivery risk: mediation analysis of prenatal care timing

2020· article· en· W3114888628 on OpenAlexaff
Khalidha Nasiri, Erica E. M. Moodie, Haim A. Abenhaim

Bibliographic record

VenueJournal of Perinatal Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsMcGill UniversityJewish General HospitalWestern University
Fundersnot available
KeywordsMedicinePrenatal careObstetricsPopulationEthnic groupCohortDemographyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: We estimated the degree to which the association between race and spontaneous recurrent preterm delivery is mediated by the timing of the first prenatal care visit. METHODS: A retrospective population-based cohort study was conducted using the U.S. National Center for Health Statistics Natality Files. We identified 644,576 women with a prior PTB who delivered singleton live neonates between 2011 and 2017. A mediation analysis was conducted using log-binomial regression to evaluate the mediating effect of timing of first prenatal care visit. RESULTS: During the seven-year period, 349,293 (54.2%) White non-Hispanic women, 131,296 (20.4%) Black non-Hispanic women, 132,367 (20.5%) Hispanic women, and 31,620 (4.9%) Other women had a prior preterm delivery. The risk of late prenatal care initiation was higher in Black non-Hispanic women, Hispanic women, and Other women (women of other racial/ethnic backgrounds) compared to White non-Hispanic women, and the risk of preterm delivery was higher in women with late prenatal care initiation. Between 8 and 15% of the association between race and spontaneous recurrent preterm delivery acted through the delayed timing of the first prenatal care visit. CONCLUSIONS: Racial disparities in spontaneous recurrent preterm delivery rates can be partly, but not primarily, attributed to timing of first prenatal care visit.

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)0.000
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 score0.213
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.036
GPT teacher head0.343
Teacher spread0.307 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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