Nutritional Strategies Prescribed During Pregnancy and Weight Gain in Women with Gestational Diabetes Mellitus: A Systematic Review of Observational Studies
Bibliographic record
Abstract
Background/Objectives: This systematic review aims to identify diets related to weight gain in pregnant women diagnosed with gestational diabetes mellitus (GDM). Methods: This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, and its protocol was registered on the International Prospective Register of Systematic Reviews (CRD42023432322). The searches used the medical subject headings in the PubMed/MEDLINE, Web of Science, Scopus, and EMBASE databases. Studies were selected, and data were extracted by three researchers. The Newcastle–Ottawa Scale (NOS) and the Joanna Briggs Institute (JBI) tool were used to assess methodological quality. Results: Six articles were included, most of them of the cohort type, with nutritional strategies lasting 2–15 weeks for overweight/obese women, based on the “macronutrient-adjusted diet” and “calorie-adjusted diet”. Only one study addressed dietary counseling in weight management, and none considered the dietary pattern. The gestational weight gain was 4.91–13.8 kg, and a lower weight gain was found in all studies that used the “macronutrient-adjusted diet” nutritional strategy. However, it did not meet the gestational weight gain targets. Conclusions: Despite the limited number of studies examining the impact of nutritional strategies on weight gain in women with GDM, some research suggests that diets focused on macronutrient adjustment may lead to less weight gain but are not adequate. Therefore, future studies are needed to evaluate which type of nutritional strategies ensure weight gain control during pregnancy.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".