Prevention of NSAID-related upper gastrointestinal toxicity: a meta-analysis of traditional NSAIDs with gastroprotection and COX-2 inhibitors
Bibliographic record
Abstract
BACKGROUND: Traditional NSAIDs (tNSAIDs) and COX-2 inhibitors (COX-2s) are important agents for the treatment of a variety or arthritic conditions. The purpose of this study was to systematically review the effectiveness of misoprostol, H2-receptor antagonists (H2RAs), and proton pump inhibitors (PPIs) for the prevention of tNSAID related upper gastrointestinal (GI) toxicity, and to review the upper gastrointestinal (GI) safety of COX-2s. METHODS: An extensive literature search was performed to identify randomized controlled trials (RCTs) of prophylactic agents used for the prevention of upper GI toxicity, and RCTs that assessed the GI safety of the newer COX-2s. Meta-analysis was performed in accordance with accepted techniques. RESULTS: 39 gastroprotection and 69 COX-2 RCTs met inclusion criteria. Misoprostol, PPIs, and double doses of H2RAs are effective at reducing the risk of both endoscopic gastric and duodenal tNSAID-induced ulcers. Standard doses of H2RAs are not effective at reducing the risk of tNSAID-induced gastric ulcers, but reduce the risk of duodenal ulcers. Misoprostol is associated with greater adverse effects than the other agents, particularly at higher doses. COX-2s are associated with fewer endoscopic ulcers and clinically important ulcer complications, and have fewer treatment withdrawals due to GI symptoms than tNSAIDS. Acetylsalicylic acid appears to diminish the benefit of COX-2s over tNSAIDs. In high risk GI patients, tNSAID with a PPI or a COX-2 alone appear to offer similar GI safety, but a strategy of a COX-2 with a PPI appears to offer the greatest GI safety. CONCLUSION: Several strategies are available to reduce the risk of upper GI toxicity with tNSAIDs. The choice between these strategies needs to consider patients' underlying GI and cardiovascular risk.
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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".