Breastfeeding predicts the risk of childhood obesity in a multi-ethnic cohort of women with diabetes
Bibliographic record
Abstract
OBJECTIVE: To determine whether breastfeeding reduced the risk of childhood obesity in the infants of a multi-ethnic cohort of women with pregestational diabetes. METHODS: In this retrospective cohort study, women with pregestational diabetes were mailed a questionnaire about breastfeeding and current height and weight of mothers and infants. Predictors of obesity (weight for age >85 percentile) were assessed among offspring of index pregnancies, using univariate and multivariable logistic regression. RESULTS: Of 125 women, 81 (65%) had type 1 diabetes and 44 (35%) had type 2 diabetes. The mean age of offspring was 4.5 years. On univariate analysis, significant predictors of obesity in offspring were type 2 diabetes (odds ratio, OR 2.4, 95% confidence interval, CI 0.99-5.72); maternal body mass index (BMI)>25 (OR 4.4, 95% CI 1.4-19.4); and any breastfeeding (OR 0.22, 95% CI 0.07-0.72). After multivariable adjustment, breastfeeding (OR 0.20, 95% CI 0.06-0.69) and having an overweight/obese mother (OR 3.49, 95% CI 1.03-16.2) remained independently associated with childhood obesity. CONCLUSION: Breastfeeding significantly decreased the likelihood of obesity in offspring of mothers with pregestational diabetes, independent of maternal BMI and diabetes type. Women with diabetes should be encouraged to breastfeed, given the increased risk of obesity in their children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".