Overview review: Comparative efficacy of oral ibuprofen and paracetamol (acetaminophen) across acute and chronic pain conditions
Bibliographic record
Abstract
BACKGROUND: Ibuprofen and paracetamol have long been used as analgesics in a range of acute, intermittent and chronic pain conditions. Paracetamol is often the first line analgesic recommended, without consensus about which is the better analgesic. METHODS: An overview review of systematic reviews and meta-analyses directly compares ibuprofen and paracetamol at standard doses in particular painful conditions, or uses indirect comparisons against placebo. Electronic searches for systematic reviews were sought published since 1995 using outcomes approximating to ≥50% pain intensity reduction. Painful conditions were acute post-operative pain, dysmenorrhoea, tension-type headache (TTH), migraine, osteoarthritis and rheumatoid arthritis, back pain, cancer and paediatric pain. There was no systematic assessment of harm. RESULTS: Sixteen systematic reviews and four individual patient data meta-analyses were included. Ibuprofen was consistently superior to paracetamol at conventional doses in a range of painful conditions. Two direct comparisons favoured ibuprofen (acute pain, osteoarthritis). Three of four indirect comparisons favoured ibuprofen (acute pain, migraine, osteoarthritis); one showed no difference (TTH), although there were methodological problems. In five pain conditions (dysmenorrhoea, paediatric pain, cancer pain, back pain and rheumatoid arthritis), there were limited data on paracetamol and ibuprofen. CONCLUSIONS: At standard doses in different painful conditions, ibuprofen was usually superior producing more patients with the degree of pain relief that patients feel worthwhile. Neither of the drugs will be effective for everyone, and both are needed. This overview questions the practice of routinely using paracetamol as a first line analgesic because there is no good evidence for efficacy of paracetamol in many pain conditions.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".