Guidelines for the management of pregnant women with obesity: A systematic review
Bibliographic record
Abstract
Summary Multiple clinical practice guidelines (CPGs) have been established for pregnant women with obesity. The quality and consistency of recommendations remain unknown. The objective of this study is to conduct a systematic review to synthesize and appraise evidence from CPGs, available worldwide, for pregnant women affected by obesity. An experienced information specialist performed a rigorous search of the literature, searching MEDLINE, Embase, grey literature, and guideline registries to locate CPGs that reported on pregnancy care relating to obesity. CPGs related to antenatal care of pregnant women with obesity (pre‐pregnancy body mass index [BMI] ≥ 30 kg/m2) in low‐risk (eg, care provider = family physician or midwife) or high‐risk settings (eg, obstetrician or maternal fetal medicine) were included. CPGs were appraised for quality with independent data collection by two raters. Information was categorized into five domains: preconception care. care during pregnancy, diet and exercise during pregnancy, care immediately before, during, and after delivery, and postpartum care. The literature search yielded 2614 unique citations. Following screening of abstracts and full texts, 32 CPGs were included, with quality ranging between 0 and 100 on the AGREE II tool. The strongest evidence related to nutritional advice, exercise, and pregnancy risk counselling. Guidance was limited for timing of screening tests, antenatal visits and delivery, ideal postpartum care, and management of adverse pregnancy outcomes. Most guidelines in this population are not evidence based. Research is needed to bridge knowledge gaps pertaining to fetal antenatal surveillance, management of adverse outcomes and postpartum care, and enhance consistency across CPGs.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".