Treatment of osteoarthritis of the hip and knee: a comparison of NSAID use in patients for whom surgery was and was not recommended.
Bibliographic record
Abstract
OBJECTIVE: To determine if NSAID use was different between OA (hip and/or knee) patients treated surgically to those treated medically. METHODS: We conducted a case control study, in which cases (n = 433) had had a total joint replacement within a two-year period, while controls (n = 195) had seen a rheumatologist or orthopedic surgeon, and not been recommended for surgery. Current and previous NSAID use was surveyed. RESULTS: Cases were older than controls (70 vs. 64 years, p < 0.0001), and were more likely to have OA in the hips (45% vs. 21%, p < 0.0001), to have severe OA (p < 0.0001), and to be male (42% vs. 28%, p < 0.0008). Potential confounding variables were statistically adjusted using logistic regression. Although disease duration was similar in cases and controls (9.8 years), cases had tried fewer NSAIDs (1.3 +/- 0.05 vs. 2.3 +/- 0.08 in controls, p < 0.0001). Cases were less likely to have taken any NSAID (86% vs. 94% of controls; OR 0.40, p < 0.007) or to have had intra-articular steroids (OR 0.19, p < 0.0001). Two or more NSAIDs were used (ever) in 38% of cases vs. 70% of controls (p < 0.0001); and 3 or more NSAIDs in 5% vs. 38% (p < 0.0001). Women were less apt to have obtained total joint replacements (OR 0.62, p < 0.0001), including TKRs even when adjusting for severity of OA. CONCLUSIONS: NSAIDs are used less by orthopedic surgeons than rheumatologists in our centre. Some subjects were offered a joint replacement without even a failure of medical management. The reasons for differences in prescribing trends are unknown. Referral biases may exist.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".