Gestational weight gain and preterm birth in obese women: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Prepregnant obesity is a global concern and gestational weight gain has been found to influence the risks of preterm birth. OBJECTIVE: To assess the relationship between gestational weight gain and risk for preterm birth in obese women. SEARCH STRATEGY: Four electronic databases were searched from 18 February through to 28 April 2015. SELECTION CRITERIA: Primary research reporting preterm birth as an outcome in obese women and gestational weight gain as a variable that could be compared to the 2009 Institute of Medicine's recommendations. DATA COLLECTION AND ANALYSIS: Two reviewers independently assessed trials for inclusion. The Newcastle Ottawa Scale was used to assess study bias. MAIN RESULTS: Our search identified six studies meeting the inclusion criteria; five were conducted in the USA and one in Peru. Four studies with a total of 10 171 obese women were meta-analysed. Significant heterogeneity was found between studies in the pooled analysis. Results for indicated preterm birth in obese women with gestational weight gain above the Institute of Medicine's recommendations showed increased risk (adjusted odds ratio 1.54; 95% CI 1.09-2.16). CONCLUSIONS: Available science on this topic is limited to special populations of obese pregnant women. Generalisable research is needed to assess the variation in risk for preterm birth in obese women by differences in gestational weight gain and class of obesity controlling for significant variables in the pathway to preterm birth. This research has the potential to illuminate new science impacting preterm birth and interventions for prevention.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".