The association between maternal and offspring preterm birth. Results from a sibling design
Bibliographic record
Abstract
IntroductionPrevious studies have reported an intergenerational association between maternal and offspring preterm birth (PTB) but the nature of the association remains unclear. Objectives and ApproachThe objective was to revisit the association between maternal and offspring preterm birth using a quasi-experimental sibling design and distinguishing between preterm birth types. We conducted a retrospective intergenerational cohort study of 39,573 women born singleton in Manitoba, Canada (1980-2002) who gave birth to 79,198 singleton infants (1995-2016). To account for familial confounding we defined a matched subcohort of 1033 sister mothers with discordant PTB status and compared offspring PTB rates between 2,499 differentially exposed cousins using log-binomial fixed-effects generalized estimating equation models. PTB was defined as a delivery < 37 gestation weeks, divided into spontaneous and provider-initiated. ResultsIn the population, mothers born preterm were more likely to give birth preterm [Adjusted Relative Risk (ARR): 1.39; 95\% Confidence Interval (CI): 1.25, 1.54]. The intergenerational association was not apparent among births to sisters with discordant PTB status [ARR: 1.02; 95\% CI: 0.77, 1.34]. However, the lack of association in the sibling analyses is explained by the fact that infants whose maternal aunts, but not their mothers, were born preterm had similarly elevated risk of PTB (10\%) than infants whose mothers were born preterm. Intergenerational patterns were observed for spontaneous PTB but not for provider-initiated PTB. Conclusion/ImplicationsThese findings suggest that it is not the fact of having been born preterm that puts women at higher risk of delivering preterm, but the fact of having been born to a mother who ever delivered a preterm baby. Consideration of family history of PTB may better identify women-at-risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".