The INTERGROWTH‐21st gestational weight gain standard and interpregnancy weight increase: A population‐based study of successive pregnancies
Bibliographic record
Abstract
Objective To link the INTERGROWTH‐21st gestational weight gain standard with the risks of excess maternal postpartum weight retention, approximated by women's weight change between successive pregnancies. Methods A population‐based retrospective cohort study of 58,534 women delivering successive pregnancies in British Columbia, Canada (2000‐2015) was conducted. Pregnancy weight gain (kg) in the index pregnancy was converted into a gestational age‐standardized z‐score using the INTERGROWTH‐21st standard. Excess interpregnancy weight gain was defined as weight increases of 5 kg, 10 kg, or obesity (≥30 kg/m2) at the next pregnancy. Weight gain z‐scores and excess interpregnancy weight change were associated using logistic regression. Results For all definitions of excess interpregnancy weight gain, risks remained low and stable below a weight gain z‐score of 0 (50th percentile) but rose sharply with increasing z‐scores above zero. Compared with women gaining −1 to 0 SD (16th to 50th percentiles), women gaining > 0 to +1 SD (51st to 84th percentiles) were 55% to 84% more likely to retain excess weight between pregnancies. Risks were three‐ to sixfold higher in women gaining >+1 SD. Conclusions A large range of the INTERGROWTH‐21st percentiles were associated with increased risks of excess interpregnancy weight gain. The standard may normalize high weight gains of women at increased risk of excess weight retention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".