Intertwin birthweight discordance and parental race: a retrospective cohort study
Bibliographic record
Abstract
Objectives: Previous studies examined the association of maternal race with pregnancy outcomes. The study aimed to examine the effects of maternal, paternal, and parental race on intertwin birth weight discordance.Methods: We used the 2011–2015 multiple birth files of the USA for this study. The exposure variable of this study was parental race and intertwin birthweight discordance >25% was the outcome. In addition to separately analyzing maternal and paternal races, we assessed the effect of the combined race after grouping the participants into nine groups based on the race of both parents.Results: A total of 203,872 pairs of twins were included in the final analysis. The overall incidence of intertwin birthweight discordance in this population was 7.8%. Birthweight discordance was significantly associated with maternal, paternal, and parental races. Twins born to Black parents had significant risks of developing birthweight discordance than twins born to White parents (adjusted OR = 1.11, 95% CI: 1.06–1.17 for Black mothers and Black fathers). Combined parental race showed that compared to twins born to both parents Whites, twins born to both parents Blacks had a significantly elevated risk of developing birthweight discordance (adjusted OR = 1.13, 95% CI: 1.07–1.18). No significant difference in the risk of developing birthweight discordance was found for other parental race groups, same race or different race parents.Conclusions: Twins born to Black mothers, fathers, or both parents had significant risks of developing intertwin birthweight discordance than twins born to White mothers, fathers, or both parents.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".