Can we safely recommend gestational weight gain below the 2009 guidelines in obese women? A systematic review and meta‐analysis
Bibliographic record
Abstract
A systematic review was conducted to determine the risk of adverse pregnancy outcomes with gestational weight gain (GWG) below the 2009 Institute of Medicine guidelines compared with within the guidelines in obese women. MEDLINE, Embase, Cochrane Register, CINHAL and Web of Science were searched from 1 January 2009 to 31 July 2014. Quality was assessed using a modified Newcastle-Ottawa scale. Three primary outcomes were included: preterm birth, small for gestational age (SGA) and large for gestational age (LGA). Eighteen cohort studies were included. GWG below the guidelines had higher odds of preterm birth (adjusted odds ratio [AOR] 1.46; 95% confidence interval [CI] 1.07-2.00) and SGA (AOR 1.24; 95% CI 1.13-1.36) and lower odds of LGA (AOR 0.77; 95% CI 0.73-0.81) than GWG within the guidelines. Across the three obesity classes, the odds of SGA and LGA did not show any notable gradient and remained unexplored for preterm birth. Decreased odds were noted for macrosomia (AOR 0.64; 95% CI 0.54-0.77), gestational hypertension (AOR, 0.70; 95% CI 0.53-0.93), pre-eclampsia (AOR 0.90; 95% CI 0.82-0.99) and caesarean (AOR 0.87; 95% CI 0.82-0.92). GWG below the guidelines cannot be routinely recommended but might occasionally be individualized for certain women, with caution, taking into account other known risk factors.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".