Gestational weight gain in twin gestations and pregnancy outcomes: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Data on the association of inappropriate gestational weight gain (GWG) and adverse outcomes in twin pregnancies are limited and inconsistent. OBJECTIVES: To perform a systematic review and meta-analysis on the association between GWG and adverse outcomes in twin pregnancies. SEARCH STRATEGY: Ovid, Medline, EMBASE and Cochrane Central databases from 1 January 1990 until 23 September 2020. SELECTION CRITERIA: Interventional and observational studies evaluating the association between GWG and adverse outcomes in twin pregnancies. DATA COLLECTION AND ANALYSIS: Data were extracted by two independent reviewers. Summary odds ratios (OR) were calculated using a random-effects model in a subset of studies that analysed GWG as a categorical variable in relation to the Institute of Medicine (IOM) recommendations. The primary outcome was preterm birth. MAIN RESULTS: From 277 citations, 19 studies involving 36 023 women with twin pregnancies were included in the qualitative analysis, of which 14 were included in the meta-analysis. Overall, 56.8% of women experienced inappropriate GWG: 35.4% (95% CI 30.0-41.0%) gained weight below and 21.4% (95% CI 14.2-29.5%) gained weight above IOM recommendations. Compared with GWG within IOM guidelines, GWG below IOM guidelines was associated with preterm birth before 32 weeks of gestation (OR 3.38; 95% CI 2.05-5.58), and a reduction in the risk of pre-eclampsia (OR 0.68; 95% CI 0.48-0.97). GWG above IOM guidelines was associated with an increased risk of pre-eclampsia that was consistent across all body mass index categories. CONCLUSIONS: Inappropriate GWG affects over half of twin pregnancies, so is a common and potentially modifiable risk factor for preterm birth and pre-eclampsia.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".