Breastfeeding and Postpartum Glucose Regulation Among Women With Prior Gestational Diabetes: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes mellitus is associated with adverse maternal and fetal outcomes and increases subsequent risk of Type 2 diabetes. Researchers have shown that breastfeeding may reduce diabetes risk in women with recent gestational diabetes. RESEARCH AIM: To assess association between infant feeding and postpartum glucose tolerance in mothers with recent gestational diabetes within 1 year postpartum. METHODS: A literature search was performed up to December 31, 2019, retrieving articles related to infant feeding, gestational diabetes, and postpartum glucose regulation in four major databases (PubMed, Cochrane, CINAHL, and Embase). Methodological quality was assessed using tools from the United States National Institutes of Health and the National Heart, Lung, and Blood Institute. RESULTS: The search yielded 15 cohort studies meeting the selection criteria. Of the 15 studies, 13 (86.7%) examined the influence of breastfeeding on postpartum glycemic status, and eight (53.4%) compared the mean blood glucose values between breastfeeding and non-breastfeeding participants. Of the 13 studies that compared postpartum glycemic status, nine (60%) of the research teams found that breastfeeding lowered rates of impaired glucose tolerance, and four (26.7%) showed no significant change. In eight of the studies reporting mean blood glucose values, six (75%) reported significantly lower fasting plasma glucose in breastfeeding participants, with reductions ranging from 3.7 to 7.4 mg/dL (0.2-0.4 mmol/L). CONCLUSION: Breastfeeding has been associated with improved postpartum glucose regulation in mothers with gestational diabetes. In pregnant women with gestational diabetes, breastfeeding may reduce the risk of Type 2 diabetes, and women with gestational diabetes should be strongly encouraged and supported to breastfeed.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".