Recategorization of Non-Aspirin Nonsteroidal Anti-inflammatory Drugs According to Clinical Relevance: Abandoning the Traditional NSAID Terminology
Bibliographic record
Abstract
Non-aspirin nonsteroidal anti-inflammatory drugs (NSAIDs) are frequently used to treat pain, fever, and inflammation. Historically, NSAIDs have been categorized as traditional NSAIDs and newer cyclooxygenase (COX)-2 inhibitors (coxibs). However, traditional NSAIDs also inhibit the COX-1 and COX-2 enzyme isoforms to a varying degree. This diversity of COX-1 and COX-2 selectivity within the class of traditional NSAIDs has proven clinically important, with evidence accumulating on the cardiovascular risks associated with selective COX-2 inhibition. Thus, the relative COX-2 selectivity of traditional NSAIDs correlates with their cardiovascular risk profile, being more favourable for non-selective NSAIDs, such as naproxen and low-dose ibuprofen, and less favourable for more COX-2 selective agents, such as diclofenac. To enhance clinically relevant terminology, we advocate categorizing all non-aspirin NSAIDs-including traditional NSAIDs-according to their relative COX-1 and COX-2 selectivity as either COX-1 inhibitors, non-selective NSAIDs, or COX-2 inhibitors. We further recommend subcategorizing COX-2 inhibitors as newer COX-2 inhibitors (coxibs) or older COX-2 inhibitors. Finally, we recommend examining the effects of the individual NSAIDs included in each of the proposed categories. Adhering to these recommendations will align future studies, advance interpretation of COX-specific adverse cardiovascular effects, and provide better guidance to clinicians prescribing NSAIDs.
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 | 0.027 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".