Canadian consensus guidelines on long‐term nonsteroidal anti‐inflammatory drug therapy and the need for gastroprotection: benefits versus risks
Bibliographic record
Abstract
BACKGROUND: Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely used, but are not without risks. AIM: To provide evidence-based management recommendations to help clinicians determine optimal long-term NSAID therapy and the need for gastroprotective strategies based on an assessment of both gastrointestinal (GI) and cardiovascular (CV) risks. METHODS: A multidisciplinary group of 21 voting participants revised and voted on the statements and the strength of evidence (assessed according to GRADE) at a consensus meeting. RESULTS: An algorithmic approach was developed to help manage patients who require long-term NSAID therapy. The use of low-dose acetylsalicylic acid in patients with high CV risk was assumed. For patients at low GI and CV risk, a traditional NSAID alone may be acceptable. For patients with low GI risk and high CV risk, full-dose naproxen may have a lower potential for CV risk than other NSAIDs. In patients with high GI and low CV risk, a COX-2 inhibitor plus a proton pump inhibitor (PPI) may offer the best GI safety profile. When both GI and CV risks are high and NSAID therapy is absolutely necessary, risk should be prioritized. If the primary concern is GI risk, a COX-2 inhibitor plus a PPI is recommended; if CV risk, naproxen 500 mg b.d. plus a PPI would be preferred. NSAIDs should be used at the lowest effective dose for the shortest possible duration. CONCLUSION: More large, long-term trials that examine clinical outcomes of complicated and symptomatic upper and lower GI ulcers are needed.
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 | 0.029 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".