The Inverse OARSI-OMERACT Criteria Is a Valid Indicator of the Clinical Worsening of Knee Osteoarthritis: Data From the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: We assessed if the inverse Osteoarthritis Research Society International (OARSI) and Outcome Measures in Rheumatology (OMERACT) criteria relate to concurrent radiographic knee osteoarthritis (KOA) progression and decline in walking speed, as well as future knee replacement. METHODS: We conducted knee-based analyses of data from the Osteoarthritis Initiative. All knees had symptomatic OA: at least doubtful radiographic KOA (Kellgren-Lawrence grade ≥ 1) and knee pain ≥ 10/100 (Western Ontario and McMaster Universities Osteoarthritis Index pain) at the 12-month visit. The inverse of the OARSI-OMERACT responder criteria depended on knee pain and function, and global assessment of knee impact. We used generalized linear mixed models to assess the relationship of the inverse OARSI-OMERACT criteria over 2 years (i.e., 12-month and 36-month visits) with worsening radiographic severity (any increase in Kellgren-Lawrence grade from 12 months to 36 months) and decline in self-selected 20-m walking speed of ≥ 0.1m/s (from 12 months to 36 months). We used a Cox model to assess time to knee replacement during the 6 years after the 36-month visit as an outcome. RESULTS: Among the 1746 analyzed, 19% met the inverse OARSI-OMERACT criteria. Meeting the inverse OARSI-OMERACT criteria was associated with almost double the odds of experiencing concurrent worsening in radiographic KOA severity (OR 1.89, 95% CI 1.32-2.70) or decline in walking speed (OR 1.82, 95% CI 1.37-2.40). A knee meeting the inverse OARSI-OMERACT criteria was more likely to receive a knee replacement after the 36-month visit (23%) compared with a nonresponder (10%; HR 2.54, 95% CI 1.89-3.41). CONCLUSION: The inverse OARSI-OMERACT criteria for worsening among people with KOA had good construct validity in relation to clinically relevant outcomes.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".