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Record W3110049936 · doi:10.1111/ppe.12716

Short interpregnancy interval and pregnancy outcomes: How important is the timing of confounding variable ascertainment?

2020· article· en· W3110049936 on OpenAlexafffundabout
Laura Schummers, Jennifer A. Hutcheon, Wendy V. Norman, Jessica Liauw, Talshyn Bolatova, Katherine A. Ahrens

Bibliographic record

VenuePaediatric and Perinatal Epidemiology · 2020
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineConfoundingConfidence intervalPregnancyObstetricsInterval (graph theory)StatisticsDemographyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Estimation of causal effects of short interpregnancy interval on pregnancy outcomes may be confounded by time-varying factors. These confounders should be ascertained at or before delivery of the first ("index") pregnancy, but are often only measured at the subsequent pregnancy. OBJECTIVES: To quantify bias induced by adjusting for time-varying confounders ascertained at the subsequent (rather than the index) pregnancy in estimated effects of short interpregnancy interval on pregnancy outcomes. METHODS: We analysed linked records for births in British Columbia, Canada, 2004-2014, to women with ≥2 singleton pregnancies (n = 121 151). We used log binomial regression to compare short (<6, 6-11, 12-17 months) to 18-23-month reference intervals for 5 outcomes: perinatal mortality (stillbirth and neonatal death); small for gestational age (SGA) birth and preterm delivery (all, early, spontaneous). We calculated per cent differences between adjusted risk ratios (aRR) from two models with maternal age, low socio-economic status, body mass index, and smoking ascertained in the index pregnancy and the subsequent pregnancy. We considered relative per cent differences <5% minimal, 5%-9% modest, and ≥10% substantial. RESULTS: Adjustment for confounders measured at the subsequent pregnancy introduced modest bias towards the null for perinatal mortality aRRs for <6-month interpregnancy intervals [-9.7%, 95% confidence interval [CI] -15.3, -6.2). SGA aRRs were minimally biased towards the null (-1.1%, 95% CI -2.6, 0.8) for <6-month intervals. While early preterm delivery aRRs were substantially biased towards the null (-10.4%, 95% CI -14.0, -6.6) for <6-month interpregnancy intervals, bias was minimal for <6-month intervals for all preterm deliveries (-0.6%, 95% CI -2.0, 0.8) and spontaneous preterm deliveries (-1.3%, 95% CI -3.1, 0.1). For all outcomes, bias was attenuated and minimal for 6-11-month and 12-17-month interpregnancy intervals. CONCLUSION: These findings suggest that maternally linked pregnancy data may not be needed for appropriate confounder adjustment when studying the effects of short interpregnancy interval on pregnancy outcomes.

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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)0.001
metaresearch head score (Gemma)0.003
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 score0.091
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.345
Teacher spread0.281 · 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

Citations11
Published2020
Admission routes3
Has abstractyes

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