Postnatal Prevention of Childhood Obesity in Offspring Prenatally Exposed to Gestational Diabetes mellitus: Where Are We Now
Bibliographic record
Abstract
Children exposed to gestational diabetes mellitus (GDM) in utero are at high risk of developing many health problems such as obesity. There is an urgent need to find new strategies to prevent obesity development among high-risk populations such as those children. Accordingly, the aim of this review was to summarize current knowledge on the postnatal prevention of childhood obesity in offspring born from mothers with GDM. Specifically, this review addresses the impact of breastfeeding, complementary feeding practices as well as dietary intake and physical activity during childhood on obesity risk of children exposed to GDM in utero. Furthermore, breast milk composition of diabetic mothers and its potential impact on growth is discussed. According to the available literature, breastfeeding may reduce obesity risk in children exposed to GDM in utero but a longer duration seems necessary to achieve its protective effect against obesity. Detailed analysis of breast milk composition of mothers with GDM will be necessary to fully understand the relationship between breastfeeding and obesity in this specific population. This review highlights the need for more studies addressing the impact of complementary feeding practices and lifestyle habits during childhood on obesity risk of children exposed to GDM in utero.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".