Paracetamol for the Management of Pain in Inflammatory Arthritis: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the literature on the efficacy and safety of paracetamol (acetaminophen) in the management of pain in inflammatory arthritis. METHODS: A systematic search was performed in Medline, Embase, the Cochrane Library, and 2008/2009 American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) conference abstracts for clinical trials and observational studies of paracetamol in patients with inflammatory arthritis. Included trials were appraised for risk of bias, and relevant study details were abstracted. Efficacy was assessed from clinical trials using improvement in pain as the outcome measure, and safety was assessed using total adverse events and withdrawals due to adverse events as outcome measures. Safety data from observational studies were assessed separately. RESULTS: Eleven articles containing 12 clinical trials and 1 observational study were identified, all in patients with rheumatoid arthritis. The trials were of short duration, used atypical doses of paracetamol, and all had a high risk of bias. Overall, there was weak evidence of a benefit of paracetamol over placebo and an additive benefit of paracetamol in combination with nonsteroidal antiinflammatory drugs (NSAID). The benefit of paracetamol to NSAID alone was uncertain. No significant differences in safety were seen in the limited clinical trial data. One cohort study showed an increased rate of serious gastrointestinal events with paracetamol over NSAID when used concurrently with corticosteroids and other analgesics, but had significant methodological limitations. CONCLUSION: There is weak evidence for the efficacy of paracetamol in patients with inflammatory arthritis, and insufficient disease-specific safety data to draw conclusions.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".