Review: paracetamol reduces pain in osteoarthritis but is less effective than NSAIDs
Bibliographic record
Abstract
Zhang W, Jones A, Doherty M. Does paracetamol (acetaminophen) reduce the pain of osteoarthritis? A meta-analysis of randomised controlled trials. Ann Rheum Dis 2004;63:901–7.[OpenUrl][1][Abstract/FREE Full Text][2] Q Is paracetamol efficacious for treatment of osteoarthritis (OA)? ### ![Graphic][3]</img>Data sources: Medline, CINAHL, EMBASE/Excerpta Medica, Scientific Citation Index, and Cochrane Library (up to July 2003); reference lists; and conference abstracts from international societies of rheumatology (previous 2 y). ### ![Graphic][4]</img>Study selection and assessment: randomised controlled trials (RCTs) that compared paracetamol with placebo or non-steroidal anti-inflammatory drugs (NSAIDs) in patients who had radiographic evidence of OA or met American College of Rheumatology clinical criteria for OA or had pain associated with OA. Quality of individual studies was assessed based on randomisation, blinding, and withdrawals. ### ![Graphic][5]</img>Outcomes: pain reduction from baseline, change in total Western Ontario and McMaster University (WOMAC) OA Index scores, change in function and stiffness, and adverse events (gastrointestinal discomfort, nausea, headache, and dizziness). 10 trials (n = 2144) met the selection criteria. … [1]: {openurl}?query=rft.jtitle%253DAnn%2BRheum%2BDis%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fard.2003.018531%26rft_id%253Dinfo%253Apmid%252F15020311%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=annrheumdis&resid=63/8/901&atom=%2Febnurs%2F8%2F1%2F21.atom [3]: /embed/inline-graphic-1.gif [4]: /embed/inline-graphic-2.gif [5]: /embed/inline-graphic-3.gif
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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".