OARSI/OMERACT Initiative to Define States of Severity and Indication for Joint Replacement in Hip and Knee Osteoarthritis. An OMERACT 10 Special Interest Group
Bibliographic record
Abstract
OBJECTIVE: To define pain and physical function cutpoints that would, coupled with structural severity, define a surrogate measure of "need for joint replacement surgery," for use as an outcome measure for potential structure-modifying interventions for osteoarthritis (OA). METHODS: New scores were developed for pain and physical function in knee and hip OA. A cross-sectional international study in 1909 patients was conducted to define data-driven cutpoints corresponding to the orthopedic surgeons' indication for joint replacement. A post hoc analysis of 8 randomized clinical trials (1379 patients) evaluated the prevalence and validity of cutpoints, among patients with symptomatic hip/knee OA. RESULTS: In the international cross-sectional study, there was substantial overlap in symptom levels between patients with and patients without indication for joint replacement; indeed, it was not possible to determine cutpoints for pain and function defining this indication. The post hoc analysis of trial data showed that the prevalence of cases that combined radiological progression, high level of pain, and high degree of function impairment was low (2%-12%). The most discriminatory cutpoint to define an indication for joint replacement was found to be [pain (0-100) + physical function (0-100) > 80]. CONCLUSION: These results do not support a specific level of pain or function that defines an indication for joint replacement. However, a tentative cutpoint for pain and physical function levels is proposed for further evaluation. Potentially, this symptom level, coupled with radiographic progression, could be used to define "nonresponders" to disease-modifying drugs in OA clinical trials.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".