Systemic use of antibiotics and risk of diabetes in adults: <scp>A</scp> nested case‐control study of <scp>Alberta's Tomorrow Project</scp>
Bibliographic record
Abstract
AIMS: Previous observational studies using administrative health records have suggested an increased risk of diabetes with use of antibiotics. However, unmeasured confounding factors may explain these results. This study characterized the association between systemic use of antibiotics and risk of diabetes in a cohort of adults in Canada, accounting for both clinical and self-reported disease risk factors. MATERIALS AND METHODS: In this nested case-control study, we used data from Alberta's Tomorrow Project (ATP), a longitudinal cohort study in Canada, and the linked administrative health records (2000-2015). Incident cases of diabetes were matched with up to 8 age and sex-matched controls per case. Conditional logistic regression was used to examine the association between antibiotic exposures and incident diabetes after sequentially adjusting for important clinical and lifestyle factors. RESULTS: This study included 1676 cases of diabetes and 13 401 controls. Although 17.9% of cases received more than 5 courses of antibiotics, compared to 13.8% of controls (P < .0001), the association between antibiotic use and risk of diabetes was progressively reduced as important clinical and lifestyle factors were accounted for. In fully adjusted models, compared to participants with 0 to 1 courses of antibiotics, participants receiving more antibiotics had no increased risk of diabetes [Odds Ratio, 0.97 (0.83-1.13) for 2 to 4 courses and 0.98 (0.82-1.18) for ≥5 courses]. CONCLUSIONS: After adjustment for clinical and difficult-to-capture lifestyle data, we found no association between systemic use of antibiotics and risk of diabetes. Our results suggest that those positive associations observed in previous studies using only administrative records might have been confounded.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".