Prevention of gestational diabetes mellitus: a review of studies on weight management
Bibliographic record
Abstract
Entering pregnancy with overweight, obesity or gaining excessive gestational weight could increase the risk of gestational diabetes mellitus (GDM), which is associated with negative consequences for both the mother and the offspring. The objective of this article was to review scientific evidence regarding the association between obesity and GDM, and how weight management through nutritional prevention strategies could prove successful in reducing the risk for GDM. Studies published between January 1975 and January 2009 on the relationship between GDM, pre-pregnancy body mass index (BMI), gestational weight gain and nutritional prevention strategies were included in this review. Results from these reports suggest that maternal obesity assessed by pre-pregnancy BMI is associated with an increased risk of GDM. They also show an association between gestational weight gain and increased risk for GDM. Higher dietary fat and lower carbohydrate intakes during pregnancy appear to be associated with a higher risk for GDM, independent of pre-pregnancy BMI. Some studies showed that restricting energy and carbohydrates could minimize gestational weight gain. However, a firm conclusion on the most effective nutritional intervention for the control of gestational weight gain and glycaemic responses could not be reached based on available studies. In light of the studies reviewed, we conclude that weight management through nutritional prevention strategies could be successful in reducing the risk of GDM. Further studies are required to identify the most effective diet composition to prevent GDM and excessive gestational weight gain.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".