Maternal age and pregnancy outcomes in twin compared with singleton gestations
Bibliographic record
Abstract
OBJECTIVE: To estimate the association of advanced maternal age with pregnancy complications in twin pregnancies and compare it with that observed in singleton pregnancies. METHODS: A population-based retrospective cohort study of all patients with a singleton or twin hospital birth in Ontario, Canada, between 2012 and 2019. The primary outcome was preterm birth (PTB) less than 34 weeks. Pregnancy outcomes were stratified by maternal age groups in twin pregnancies and, separately, in singleton pregnancies. RESULTS: A total of 935 378 patients met the study criteria: 920503 (98.4%) had a singleton pregnancy and 14 875 (1.6%) had twins. In singletons, the rate of PTB less than 34 weeks increased progressively with increasing maternal age and was highest for patients aged 45 years or more (3.4%; adjusted risk ratio [aRR] 1.56, 95% confidence interval [CI] 1.05-2.33). By contrast, in twins, although the rate of PTB less than 34 was highest patients under 20 years of age (25.3%) and was lowest among patients aged 35-39 years (11.7%), the associations between maternal age group and the risk of PTB were not statistically significant in the adjusted analysis. CONCLUSION: Although the absolute rates of pregnancy complications are higher in twin pregnancies, there are considerable differences in the relationship between maternal age and the risk of certain complications between twin and singleton pregnancies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".