Cardiovascular safety of NSAIDs: Additional insights after PRECISION and point of view
Bibliographic record
Abstract
Increasing numbers of patients with arthritis use nonsteroidal anti-inflammatory drugs (NSAIDs), some for long periods. The relative cardiovascular safety of NSAID use is of considerable concern, particularly among patients with or at risk of cardiovascular disease. Until recently, the evidence base was limited to older trials with small sample sizes. The large-scale Prospective Randomized Evaluation of Celecoxib Integrated Safety vs Ibuprofen or Naproxen (PRECISION) trial and a recent Bayesian meta-analysis of individual patient data in nearly a half-million patients were undertaken to address some of the existing gaps in knowledge relative to the cardiovascular safety of NSAID use. We reviewed the results, strengths, and limitations of PRECISION. We believe that the results of the meta-analysis will further assist clinicians in decision-making for management of patients with osteoarthritis. The totality of evidence would support avoidance of NSAID use, if possible, in patients with or at high risk for cardiovascular disease. If used, the shortest-duration and lowest effective NSAID doses should be chosen, given the evidence that risk is duration- and dose-dependent. We also provide a brief discussion of the mechanism of action of NSAIDs, along with discussion of existing guidelines and the recent meta-analysis.
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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".