Gestational weight gain and adverse pregnancy outcomes: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To assess the associations of gestational weight gain (GWG) in early and late pregnancy with subsequent risks of adverse pregnancy outcomes in Chinese women. DESIGN: Prospective cohort study. SETTING: Shanghai, China. PARTICIPANTS: We studied 2630 nulliparous singleton pregnant women with complete data on weight gain in early (≤17 weeks of gestation) and late (>17 weeks) pregnancy in the Shanghai Birth Cohort. METHODS: GWG was standardised into z-scores by gestational age and categorised as low (z-score <-1), normal (-1 to +1) and high (>1). The adjusted relative risks (aRRs) and 95%CIs were estimated through log-binomial regression models. Interaction effects between GWG and some other adjustment factors were tested, further stratified analyses were performed separately where interaction terms were significant. OUTCOME MEASURES: Adverse maternal and neonatal outcomes. RESULTS: Independent from GWG in late pregnancy, higher GWG in early pregnancy was associated with higher risks of gestational diabetes mellitus (aRR: 1.66; 95% CI: 1.11 to 2.48), caesarean section (aRR: 1.21; 95% CI: 1.05 to 1.39) and prolonged hospitalisation (aRR: 1.56; 95% CI: 1.03 to 2.38). Higher GWG in late pregnancy was independently associated with higher risks of caesarean section (aRR: 1.24; 95% CI: 1.09 to 1.41), large for gestational age (aRR: 2.01; 95% CI: 1.50 to 2.7) and macrosomia (aRR: 1.90; 95% CI: 1.30 to 2.78). In addition, the risk of gestational hypertension increased significantly with increased total GWG (aRR: 1.78; 95% CI: 1.14 to 2.76). The effects of GWG in late pregnancy on maternal and neonatal outcomes were significantly different between the women bearing a female and the women bearing male fetus. CONCLUSION: The GWG associations with adverse pregnancy outcomes differ at early and late pregnancy, and there may be effect modification by fetal sex in the association of GWG in late pregnancy with some pregnancy outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".