Inhaled corticosteroids and the risk of diabetes among the elderly
Bibliographic record
Abstract
AIMS: There is evidence that large doses of inhaled corticosteroids lead to an increased risk of glaucoma, cataracts and other problems associated with oral corticosteroid use. However, no formal investigation so far has been conducted into the relationship between inhaled corticosteroids and diabetes. METHODS: Our nested case-control design studied the association between current use of inhaled corticosteroids and the risk of using antidiabetic medications among a cohort of 21 645 elderly subjects. We also investigated the possibility of a dose-response relationship in users of beclomethasone. Data were obtained from the medical and pharmaceutical databases of the Regie de l'assurance maladie du Québec. RESULTS: Within the cohort, we identified 1494 cases and we selected 14 931 controls using density sampling. The unadjusted rate ratio (and 95% confidence interval, CI) for developing diabetes among current users of inhaled corticosteroids was 1.4 (1.2, 1.5). After adjusting for covariates, the rate ratio (95% CI) decreased to 0.9 (0.8, 1.1). The loss of statistical significance was due in large part to adjusting for the current use of oral corticosteroids. We also did not observe a statistically significant increase in risk among users of high-dose beclomethasone compared to nonusers, after adjusting for covariates. CONCLUSIONS: Our results do not indicate an increased risk of diabetes among current users of inhaled corticosteroids.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".