Contribution of prepregnancy body mass index and gestational weight gain to caesarean birth in Canada
Bibliographic record
Abstract
BACKGROUND: Overweight and obese women are known to be at increased risk of caesarean birth. This study estimates the contribution of prepregnancy body mass index (BMI) and gestational weight gain (GWG) to caesarean births in Canada. METHODS: We analyzed data from women in the Canadian Maternity Experiences Survey who had a singleton term live birth in 2005-2006. Adjusted odds ratios for caesarean birth across BMI and GWG groups were derived, separately for nulliparous women and parous women with and without a prior caesarean. Population attributable fractions of caesarean births associated with above normal BMI and excess GWG were calculated. RESULTS: The overall caesarean birth rate was 25.7%. Among nulliparous and parous women without a previous caesarean birth, rates in obese women were 45.1% and 9.7% respectively, and rates in women who gained above their recommended GWG were 33.5% and 8.0% respectively. Caesarean birth was more strongly associated with BMI than with GWG. However, due to the high prevalence of excess GWG (48.8%), the proportion of caesareans associated with above normal BMI and excess GWG was similar [10.1% (95% CI: 9.9-10.2) and 10.9% (95% CI: 10.7-11.1) respectively]. Overall, one in five (20.2%, 95% CI: 20.0-20.4) caesarean births was associated with above normal BMI or excess GWG. CONCLUSIONS: Overweight and obese BMI and above recommended GWG are significantly associated with caesarean birth in singleton term pregnancies in Canada. Strategies to reduce caesarean births must include measures to prevent overweight and obese BMI prior to conception and promote recommended weight gain throughout pregnancy.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".