A comparison of the efficacy and safety of nonsteroidal antiinflammatory agents versus acetaminophen in the treatment of osteoarthritis: A meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To perform a meta-analysis comparing the efficacy and safety of recommended dosages of nonsteroidal antiinflammatory drugs (NSAIDs), including cyclooxygenase 2 inhibitors, versus acetaminophen in the treatment of symptomatic hip and knee osteoarthritis. METHODS: Medline and EMBASE searches were performed for original clinical trials directly comparing NSAIDs with acetaminophen. A standardized form was used to abstract all data, including outcome measures of pain at rest, walking pain, and dropouts due to adverse effects. Inverse-variance-weighted mean differences (WMDs) and 95% confidence intervals (95% CI) for pain measures were determined for treatment groups. Odds ratios (ORs) and 95% CIs were calculated for withdrawals due to adverse events. Results were compared using a random effects model. RESULTS: Seven articles met inclusion criteria with sufficient data for analysis. Participants had a mean age of 61.1 years and 71.1% were women. Test of heterogeneity was not significant for either rest (P = 0.73) or walking (P = 0.76) pain. The scores for overall pain at rest (WMD -6.33 mm on a 100-mm visual analog scale [VAS], 95% CI -9.24, -3.41) and walking pain (WMD -5.76 mm on a 100-mm VAS, 95% CI -8.99, -2.52) favored the NSAID-treated group. Although NSAIDs elevated the risk of withdrawals due to adverse events, the difference was not statistically significant (OR 1.45, 95% CI 0.93, 2.27). CONCLUSION: NSAIDs are statistically superior in reducing rest and walking pain compared with acetaminophen for symptomatic osteoarthritis. Safety, measured by discontinuation due to adverse events, was not statistically different between NSAID- and acetaminophen-treated groups.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.069 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".