Mechanisms of Nonsteroidal Anti‐Inflammatory Drug‐Induced Gastrointestinal Injury and Repair: A Window of Opportunity for Cyclooxygenase‐Inhibiting Nitric Oxide Donors
Bibliographic record
Abstract
Nonsteroidal anti-inflammatory drugs (NSAIDs) cause damage in the upper gastrointestinal (GI) tract by impairing the ability of the mucosa to resist and respond to injury. Many of these effects of NSAIDs can be attributed to their ability to suppress mucosal prostaglandin synthesis. Selective inhibitors of cyclooxygenase (COX)-2 are less likely to disrupt mucosal defence and do not interfere with platelet aggregation. Thus, their use is associated with a reduced incidence of serious GI adverse events; however, a significant risk of such events still persists. At least in animal models, selective COX-2 inhibitors interfere with ulcer healing to the same extent as conventional NSAIDs. In contrast, COX-inhibiting nitric oxide donors (CINODs) produce anti-inflammatory and analgesic effects comparable or superior to those of NSAIDs, but with greatly reduced GI toxicity. Unlike NSAIDs and selective COX-2 inhibitors, CINODs do not interfere with ulcer healing. Moreover, because CINODs suppress the activity of both COX-1 and COX-2, they do not share with selective COX-2 inhibitors the lack of cardioprotection afforded by significant suppression of platelet aggregation. Because of their safety profile, CINODs may be particularly useful for long term prevention applications, such as for colon cancer, cardiovascular disease and Alzheimer's disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".