Breastfeeding patterns of mothers with type 1 diabetes: results from an infant feeding trial
Bibliographic record
Abstract
BACKGROUND: Both the initiation and maintenance of breastfeeding have been reported to be negatively affected by maternal type 1 diabetes (T1D). The aim of this study was to prospectively examine the breastfeeding patterns among mothers with and without T1D participating in a large international randomized infant feeding trial (TRIGR). METHODS: Families with a member affected by T1D and with a newborn infant were invited into the study. Those who had HLA-conferred genetic susceptibility for T1D tested at birth with gestation > 35 weeks and were healthy were eligible to continue in the trial. Among the 2160 participating children, 1096 were born to women with T1D and 1064 to unaffected women. Information on infant feeding was acquired from the family by frequent prospective dietary interviews. RESULTS: Most (>90%) of the infants of mothers with and without T1D were initially breastfed. Breastfeeding rates declined more steeply among mothers with than without T1D being 50 and 72% at 6 months, respectively. Mothers with T1D were younger, less educated and delivered earlier and more often by caesarean section than other mothers (p < 0.01). After adjusting for all these factors associated with the termination of breastfeeding, there was no difference in the duration of breastfeeding among mothers with and without T1D. CONCLUSIONS: Maternal diabetes status per se was not associated with shorter breastfeeding. The lower duration of breastfeeding in mothers with T1D is largely explained by their more frequent caesarean sections, earlier delivery and lower age and education.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".