Delayed Childbearing and Its Impact on Population Rate Changes in Lower Birth Weight, Multiple Birth, and Preterm Delivery
Bibliographic record
Abstract
OBJECTIVE: This study quantified the impact of delayed childbearing (maternal age greater-than-or-equal to 35 years) on population rate changes in low birth weight (LBW; < 2500 g), preterm delivery (< 37 weeks), multiple births, and small for gestational age (SGA; < 10th percentile) in Alberta, Canada, between 1990 (N = 42 930) and 1996 (N = 37 710). METHODS: Data were obtained from the provincial notification of a live or stillbirth. Analyses included relative risk estimates and chi(2) tests for trend. Potential confounding attributable to in vitro fertilization was investigated. RESULTS: The proportion of births to women greater-than-or-equal to 35 years of age was 8.4% in 1990 and 12.6% in 1996, a 51.2% increase. Among these women, LBW delivery increased 11%, and preterm delivery increased 14%. Delayed childbearing accounted for 78% of the change in LBW rate in the population and 36% of the change in preterm delivery rate in the population. Provincial multiple birth rates increased by 15% for twins and 14% for triplets. Delayed childbearing accounted for 15% of the twin increase and 69% of the triplet increase. When in vitro fertilization pregnancies were excluded, the change was 43% for preterm rates, 100% for LBW, 14% for twins, and 9% for triplets. Delayed childbearing did not contribute to changes in singleton SGA deliveries. CONCLUSIONS: The findings suggest that the recent increase in LBW and preterm delivery is partly related to the population phenomenon of delayed childbearing. Maternal age was not related to changes in SGA, suggesting that the age effect is through pregnancy complications that lead to preterm delivery and LBW. Prospective parents should be informed about the higher risk for neonatal morbidity associated with delayed childbearing. Health care providers should be aware of the impact of delayed childbearing on health care resources.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".