The Impact of Excessive Gestational Weight Gain on Adverse Perinatal Outcomes: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: The purpose of this study was to systematically review the potential effects of a pregnant woman’s excessive gestational weight gain on adverse perinatal outcomes affecting the mother and the fetus/neonate. Methods: Medline/PubMed, Scopus, CADTH Grey Matters and National Archive of PhD Theses were systematically searched for all relevant studies published. Assessments of the risk of bias in the included studies were made according to the tool “The Newcastle–Ottawa Scale (NOS)”. Results: Five publications met all the inclusion criteria and were included in this review. The risk of bias in all the included studies was low. One study supports the detrimental effect of excessive gestational weight gain on the risk of gestational diabetes mellitus, one study on the risk of preterm birth, five studies on the risk of macrosomia-high birthweight of the neonate, three studies on the risk of a large-for-gestational-age neonate, three studies on the risk of hypertensive disorders of pregnancy, one study on the risk of gestational hypertension and preeclampsia, five studies on the risk of delivery by cesarean section and one study on the risk of neonatal hypoglycemia requiring treatment. One study supports the beneficial effect of excessive gestational weight gain on the risk of preterm birth, two studies on the risk of a small-for-gestational-age neonate and two studies on the risk of low birthweight of the neonate. Conclusions: The study presents the most recent and strong evidence regarding the negative effect of excessive gestational weight gain on most adverse perinatal outcomes. However, excessive gestational weight gain has a beneficial effect in a very limited number of outcomes.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".