Bibliographic record
Abstract
Nonsteroidal anti-inflammatory drugs (NSAIDs) are among the most commonly prescribed medications purchased over the counter to treat acute and chronic pain and inflammation associated with a range of medical conditions.1 It is estimated that NSAIDs are prescribed to about 25% of Canadians for short-term use, but overall use is likely much higher with over-the-counter availability.2 Like any medication, the benefits of NSAIDs should be considered in tandem with the potential adverse effects. Side effects range from the mild and common to the severe and infrequent: dyspepsia, gastric or duodenal ulceration, sodium retention and subsequent hypertension, as well as increased incidence of cardiovascular (CV) adverse events. It was the withdrawal of rofecoxib from the market that brought to light the CV risk of NSAIDs and, in fact, a black-box warning now exists in NSAID product monographs, advising caution when prescribing to patients with ischemic heart disease, cerebrovascular disease and/or congestive heart failure.3 This article focuses on the CV effects associated with NSAID use by reviewing the recent literature on this topic.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".