Dietary compliance in a randomized double‐blind infant feeding trial during infancy aiming at prevention of type 1 diabetes
Bibliographic record
Abstract
The international Trial to Reduce IDDM in the Genetically at Risk (TRIGR) tested the hypothesis whether extensively hydrolyzed casein-based versus regular cow's milk-based infant formula reduces the risk of type 1 diabetes. We describe dietary compliance in the trial in terms of study formula intake, feeding of nonrecommended foods, and serum cow's milk antibody concentration reflecting intake of cow's milk protein among 2,159 eligible newborn infants with a biological first-degree relative affected by type 1 diabetes and with HLA-conferred susceptibility to type 1 diabetes. The participating infants were introduced to the study formula feeding at the median age of 15 days with a median duration of study formula use of 63 days. During the intervention, 80% of the infants received study formula. Of these, 57% received study formula for at least 2 months. On average, 45.5 l of study formula were used per infant. Only 13% of the population had received a nonrecommended food by the age of 6 months. The dietary compliance was similar in the intervention and control arm. The reported cow's milk consumption by the families matched very well with measured serum casein IgA and IgG antibody concentration. To conclude, good compliance was observed in this randomized infant feeding trial. Compliance varied between the regions and those infants who were breastfed for a longer period of time had a shorter exposure to the study formula. High dietary compliance in infant feeding trial is necessary to allow accurate interpretation of study results.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".