Food Consumption and the Risk of Type 1 Diabetes in Children and Youth: A Population-Based, Case-Control Study in Prince Edward Island, Canada
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine if the consumption of certain foods during the year prior to diagnosis of type 1 diabetes mellitus (T1D) was associated with the risk of developing T1D in children and youth residing in Prince Edward Island, Canada. METHODS: Cases (n = 57) consisted of newly diagnosed patients with T1D during 2001 to 2004. Controls (n = 105) were randomly selected from the province's population, and matched to cases by age at diagnosis and sex. Food consumption in cases and controls was assessed using two previously validated food frequency questionnaires, and a survey was developed to collect information on potential environmental and genetic risk factors. RESULTS: The median age at diagnosis was nine years, and 67% of cases were male. After controlling for the matched variables and four significant environmental and genetic risk factors (family members with T1D, the number of infections during the first two years of life, place of residence, and father's education) in the final logistic regression model, the consumption of regular soft drinks (OR = 2.78, 95% CI = 1.21, 6.36) and eggs (OR = 2.50, 95% CI = 1.09, 5.75) were significant risk factors of T1D, when consumed once per week or more often. CONCLUSION: Diet may play a role in the development of T1D. However, further research is needed to confirm these observed associations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".