The Effect of Day Care Exposure on the Risk of Developing Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Exposure to infections in infancy or childhood may be important in the pathogenesis of type 1 diabetes, but a protective role has also been suggested. We tested the hypothesis that increased early contact with infectious agents, measured by day care exposure, would decrease the risk of type 1 diabetes in childhood. RESEARCH DESIGN AND METHODS: We conducted a systematic review of case-control studies. Meta-analysis was performed to combine results, assess for heterogeneity, and explore variation in study design. RESULTS: Several generally well-designed case-control studies show a statistically significant protective effect of day care on type 1 diabetes. However, meta-analysis revealed too much heterogeneity to accept the overall synthesis results and none of the studies used prerecorded data. Day care does seem to have a protective effect in the subgroup of children who will be diagnosed with type 1 diabetes before the age of 5 years (odds ratio = 0.6, 95% CI 0.5-0.8); however, this result is based on only two studies. CONCLUSIONS: Recall bias is one alternate explanation for these data; confirmation using prerecorded data is required. Such data could be prospectively measured in cohort studies of children at risk. We also suggest that information about day care attendance be measured in randomized trials of agents for the prevention of type 1 diabetes, as day care exposure could potentially modify the effect of the preventive agent.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".