Effects of Prescription Nonsteroidal Antiinflammatory Drugs on Symptoms and Disease Progression Among Patients With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The effect of short-term and long-term use of nonsteroidal antiinflammatory drugs (NSAIDs) on structural change is equivocal. The aim of this study was to estimate the extent to which short- and long-term use of prescription NSAIDs relieve symptoms and delay structural progression among patients with radiographically confirmed osteoarthritis (OA) of the knee. METHODS: We applied a new-user design among participants with confirmed OA not reporting NSAID use at the time of enrollment in the Osteoarthritis Initiative. Participants were evaluated for changes in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) subscales (n = 1,846) and joint space width was measured using serial radiographs and a customized software tool (n = 1,116) over 4 years. We used marginal structural modeling to estimate the effect of NSAIDs. RESULTS: Compared to participants who never reported prescription NSAID use, those reporting use at 1 or 2 assessments had no clinically important changes, but those reporting prescription NSAID use at all 3 assessments had, on average, 0.88 point improvement over the followup period (95% confidence interval [95% CI] -0.46 to 2.22) in pain, 0.72 point improvement (95% CI -0.12 to 1.56) in stiffness, and 4.27 points improvement (95% CI -0.31 to 8.84) in function. The average change in joint space width was 0.28 mm less among those reporting NSAID use at 3 assessments relative to nonusers (95% CI -0.06 to 0.62). Recent NSAID use findings were not clinically or statistically significant. CONCLUSION: Long-term, but not short-term, NSAID use was associated with an a priori-defined minimally important clinical change in stiffness, physical function, and joint space width, but not pain. While showing modest clinical importance, the estimates did not reach statistical significance.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".