Novel Framework for Measuring Whole Knee Osteoarthritis Progression Using Magnetic Resonance Imaging
Bibliographic record
Abstract
OBJECTIVE: We developed and validated a set of composite scores that combine quantitative magnetic resonance imaging (MRI)-based measurements of hyaline cartilage damage, bone marrow lesions (BMLs), and effusion-synovitis into composite scores. METHODS: We selected 300 participants (n = 100 for development cohort; n = 200 for validation cohort) from the Osteoarthritis Initiative with complete clinical, radiographic, and MRI data at baseline and 24 months. We used semiautomated programs to quantify tibiofemoral and patellar cartilage damage, BML volume, and whole-knee effusion-synovitis volume. The candidate composite scores were formed by summing changes from baseline to 24 months based on prespecified methods. We evaluated the candidate composite scores for 1) the ability to differentiate groups with and without knee osteoarthritis progression (17 radiographic and patient-reported definitions), 2) sensitivity to change (standardized response means), and 3) relative performance relating to legacy outcome measures of knee osteoarthritis progression. RESULTS: Three of 13 developed composite scores qualified for testing in the validation cohort (ranked by sensitivity to change): whole-knee cumulative cartilage damage, unweighted total knee score, and BML plus effusion-synovitis volume. Change in cumulative cartilage damage associated with radiographic progression (Kellgren/Lawrence grade: odds ratio [OR] 1.84; joint space width progression: OR 2.11). Changes in the unweighted total knee score (OR 1.97) and BML plus effusion-synovitis score (OR 1.92) associated with Western Ontario and McMaster Universities Osteoarthritis Index knee pain progression. CONCLUSION: Two composite scores emerged, reflecting discrete domains of knee osteoarthritis progression. First, cumulative damage, which is measured by a whole-knee cartilage damage score, reflects the damage accrued over time. Second, dynamic disease activity, which is measured by a BML plus effusion-synovitis score, relates to changes in a patient's state of disease and symptoms.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".