Association of Selective and Conventional Nonsteroidal Antiinflammatory Drugs with Acute Renal Failure: A Population-based, Nested Case-Control Analysis
Bibliographic record
Abstract
Conventional nonsteroidal antiinflammatory drugs (NSAIDs) are associated with acute renal failure, but cyclooxygenase-2 inhibitors have not been comparatively evaluated. The authors conducted a nested case-control study to assess the association between exposure to NSAIDs, including cyclooxygenase-2 inhibitors, and hospitalization for acute renal failure. They identified 121,722 new NSAID users older than age 65 years from the administrative health care databases of Quebec, Canada, in 1999-2002. Data for 4,228 cases and 84,540 controls matched on age and follow-up time were analyzed by using conditional logistic regression, adjusted for sex, age, health status, health care utilization measures, exposure to contrast agents, and nephrotoxic medications. The risk of acute renal failure for all NSAIDs combined was highest within 30 days of treatment initiation (adjusted rate ratio (RR) = 2.05, 95% confidence interval (CI): 1.61, 2.60) and receded thereafter. The association with acute renal failure within 30 days of therapy initiation was comparable for rofecoxib (RR = 2.31, 95% CI: 1.73, 3.08), naproxen (RR = 2.42, 95% CI: 1.52, 3.85), and nonselective, non-naproxen NSAIDs (RR = 2.30, 95% CI: 1.60, 3.32) but was borderline lower for celecoxib (RR =1.54, 95% CI: 1.14, 2.09; test for interaction comparing celecoxib with rofecoxib, p = 0.057). There was a significant association for both selective and nonselective NSAIDs with acute renal failure, but confirmatory studies are required.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".