How Do Gastrointestinal or Liver Comorbidities Influence the Choice of Pain Treatment in Inflammatory Arthritis? A Cochrane Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To assess efficacy and safety of pharmacological pain treatment in patients with inflammatory arthritis (IA) and gastrointestinal (GI) or liver comorbidities. METHODS: A systematic literature search was performed using Medline, Embase, and Cochrane Controlled Trial Register up to June 2010, as well as American College of Rheumatology and European League Against Rheumatism meeting abstracts (2007-2010). The population investigated was defined as patients with IA and existing or prior reported GI or liver disease treated with nonsteroidal antiinflammatory drugs (NSAID), opioids or opioid-like drugs, paracetamol, antidepressants, neuromodulators, or muscle relaxants. Outcomes of interest were defined as efficacy evaluated by common pain measures and safety evaluated by withdrawals due to adverse events, worsening of comorbidity, and mortality. RESULTS: Out of 2869 identified studies only a single open-arm trial fulfilled inclusion criteria assessing the safety and efficacy of naproxen in 58 patients with active rheumatoid arthritis and GI comorbidities. The presence of fecal occult blood was reported in 1/58 participants tested between Weeks 1 to 26 and 2/32 participants tested between Weeks 27 to 52. Over the course of the study, 7 participants (12.1%) withdrew due to adverse events; no serious adverse events were reported. Among the 14 studies excluded due to inclusion of a mixed population (osteoarthritis or other rheumatic conditions) or an intervention that was already withdrawn, 5 trials reported a higher risk of developing GI events in patients with prior GI events when treated with NSAID. CONCLUSION: Very little evidence regarding safety and efficacy of pain treatment in patients with IA and GI or hepatic comorbidities was found. In patients with a history of GI events, extrapolating from other studies, NSAID should be used cautiously since there is evidence that these patients are at a higher risk of developing adverse events.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".