The Association Between Fetal Sex and Preterm Birth in Twin Pregnancies
Bibliographic record
Abstract
OBJECTIVE: To assess the association between the fetal sex and preterm birth. METHODS: We performed a retrospective population-based cohort study using the 1995-1997 registration twin data in the United States (148,234 live-birth twin pairs). The twin pairs were divided into 3 groups: male-male (male-male), female-female, and opposite sex. We used 3 different cutoff values of preterm birth: less than 28, 32, and 36 gestational weeks. The preterm birth rates among the 3 study groups were compared, and the adjusted risk ratios (relative risk) were estimated by multiple logistic regression. RESULTS: The male-male twin pairs had the highest pre-term birth rate (less than 28 weeks: 4.9%; less than 32 weeks: 12.4%; less than 36 weeks: 40.2%), the female-female twin pairs were intermediate (less than 28 weeks: 4.1%; less than 32 weeks: 10.6%; less than 36 weeks: 37.8%), and the opposite-sex twin pairs had the lowest rate (less than 28 weeks: 4.1%; less than 32 weeks: 10.1%; less than 36 weeks: 36.8%). Adjustment for important confounding factors or excluding twin pairs born to mothers who had an induction of labor or a cesarean delivery with medical complications did not change the results. The adjusted relative risks (95% confidence intervals) were 1.19 (1.11, 1.27), 1.21 (1.16, 1.26), and 1.09 (1.07, 1.11), respectively, for male-male twins compared with the opposite-sex twins under the 3 different cutoff values of preterm births. CONCLUSION: Male sex is associated with increased risk of preterm births in twin pregnancy. LEVEL OF EVIDENCE: II-2
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".