Association of selective and conventional nonsteroidal anti-inflammatory drugs with acute renal failure
Bibliographic record
Abstract
The safety of the novel class of nonsteroidal anti-inflammatory drugs (NSAIDs), the COX-2 inhibitors, is currently debated, with the focus on their cardiovascular toxicity. Here, the association of NSAIDs with acute renal failure (ARF) was assessed in a nested case-control study using the administrative databases of Quebec. The risk of ARF 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. After at least 30 days without an NSAID-prescription, the risk had returned to baseline. The associations with ARF were comparable for rofecoxib (RR 2.31, 95%CI 1.73 - 3.08), naproxen (RR 2.42, 95%CI 1.52 - 3.85) and non-selective, non-naproxen NSAIDs (RR 2.30, 95%CI 1.60 - 3.32), but lower for celecoxib (RR 1.54, 95%CI 1.14 - 2.09). They were dose-dependent for celecoxib, naproxen, and rofecoxib. Results were confirmed when using an alternative exposure definition. Interactions between NSAIDs and aspirin, and NSAIDs and nephrotoxic drugs could not be demonstrated conclusively. There is a significant association for both selective and non-selective NSAIDs with ARE Celecoxib appears to have a more favorable renal safety profile but confirmatory studies are required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".