Associations between MRI features versus knee pain severity and progression: Data from the Vancouver Longitudinal Study of Early Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To determine associations between features of osteoarthritis (OA) on MRI and knee pain severity and knee pain progression. DESIGN: Baseline, 3.3- and 7.5-year assessments were performed for 122 subjects with baseline knee pain (age 40-79), sample-weighted for population (with knee pain) representativeness. MRIs were scored for: osteophytes (0:absent to 3:large); cartilage (0:normal to 4:full thickness defect; 0/1 collapsed); subchondral sclerosis (0:none to 3:>50% of site), subchondral cyst (0:absent to 3:severe), bone marrow lesions (0:none to 3:≥50% of site); and meniscus (0:normal to 3:maceration/resection), in 6-8 regions each. Per feature, scores were averaged across regions. Effusion/synovitis (0:absent to 3:severe) was analyzed as ≥2 vs. <2. Linear models predicted WOMAC knee pain severity (0-100), and binary models predicted 10+ (minimum perceptible clinical improvement [MPCI]) and 20+ (minimum clinically important difference [MCID]) increases. Models were adjusted for age, sex, BMI (and follow-up time for longitudinal models). RESULTS: Pain severity was associated with osteophytes (7.17 per unit average; 95% CI = 3.19, 11.15) and subchondral sclerosis (11.03; 0.68, 21.39). MPCI-based pain increase was associated with osteophytes (odds ratio per unit average 3.20; 1.36, 7.55), subchondral sclerosis (5.69; 1.06, 30.44), meniscal damage (1.68; 1.08, 2.61) and effusion/synovitis ≥2 (2.25; 1.07, 4.71). MCID-based pain increase was associated with osteophytes (3.79; 1.41, 10.20) and cartilage defects (2.42; 1.24, 4.74). CONCLUSIONS: Of the features investigated, only osteophytes were consistently associated with pain cross-sectionally and longitudinally in all models. This suggests an important role of bone in early knee osteoarthritis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".