Does prepregnancy weight change have an effect on subsequent pregnancy health outcomes? A systematic review and meta‐analysis
Bibliographic record
Abstract
Summary International guidelines recommend women with an overweight or obese body mass index (BMI) aim to reduce their body weight prior to conception to minimize the risk of adverse perinatal outcomes. Recent systematic reviews have demonstrated that interpregnancy weight gain increases women's risk of developing adverse pregnancy outcomes in their subsequent pregnancy. Interpregnancy weight change studies exclude nulliparous women. This systematic review and meta‐analysis was conducted following MOOSE guidelines and summarizes the evidence of the impact of preconception and interpregnancy weight change on perinatal outcomes for women regardless of parity. Sixty one studies met the inclusion criteria for this review and reported 34 different outcomes. We identified a significantly increased risk of gestational diabetes (OR 1.88, 95% CI 1.66, 2.14, I 2 = 87.8%), hypertensive disorders (OR 1.46 95% CI 1.12, 1.91, I 2 = 94.9%), preeclampsia (OR 1.92 95% CI 1.55, 2.37, I 2 = 93.6%), and large‐for‐gestational‐age (OR 1.36, 95% CI 1.25, 1.49, I 2 = 92.2%) with preconception and interpregnancy weight gain. Interpregnancy weight loss only was significantly associated with increased risk for small‐for‐gestational‐age (OR 1.29 95% CI 1.11, 1.50, I 2 = 89.9%) and preterm birth (OR 1.06 95% CI 1.00, 1.13, I 2 = 22.4%). Our findings illustrate the need for effective preconception and interpregnancy weight management support to improve pregnancy outcomes in subsequent 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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".