Magnetic Resonance Imaging Biomarkers of Knee Osteoarthritis Progression
Bibliographic record
Abstract
OBJECTIVE: The Foundation for the National Institutes of Health (FNIH) OA Biomarkers Consortium aims to identify, develop, and qualify biomarkers to support drug development in knee osteoarthritis (OA). The project's second phase, the PROGRESS OA study, aims to externally validate prognostic and response biomarkers identified in the earlier phase (phase 1). Here we present results assessing external validation of prognostic imaging biomarkers. DESIGN: PROGRESS OA included data from the control arms of several completed randomized controlled trials (RCTs) for symptomatic knee OA. Radiographic progression was defined as joint space width loss (JSWL) ≥0.7 mm. Symptomatic progression was defined as increase of nine or more points in Western Ontario and McMaster Universities Arthritis Index pain (0-100 scale). Imaging biomarkers included quantitative measures of cartilage thickness and semiquantitative (SQ) assessments. Associations between baseline biomarkers and outcomes over 12 to 36 months were examined using logistic regression. RESULTS: A total of 320 participants from four RCTs were included. Forty-one participants (13%) had JSWL ≥0.7 mm and 64 (20%) had worsening symptoms. In univariable logistic regression, measures of quantitative and SQ cartilage, SQ Hoffa-synovitis, effusion-synovitis, and meniscal extrusion were consistently selected to predict JSWL ≥0.7 mm, similar to phase 1. SQ Hoffa-synovitis and lateral meniscal damage were consistently selected to predict symptomatic progression. Cross-validated areas under the curve were 0.69 (95% confidence interval [CI]: 0.53-0.85) for JSWL ≥0.7 mm and 0.77 (95% CI: 0.65-0.87) for symptomatic progression. CONCLUSION: The selected prognostic imaging biomarkers are candidates for enriching OA trials for structural and/or symptomatic progressors. Ongoing work includes pursuit of formal biomarker qualification by regulatory agencies, and the use of these biomarkers to capture structural progression with high sensitivity to change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".