A whole-joint, unidimensional, irreversible, and fine-grained MRI knee osteoarthritis severity score, based on cartilage, osteophytes and meniscus (OA-COM)
Bibliographic record
Abstract
OBJECTIVE: To develop a whole-joint, unidimensional, irreversible, and fine-grained MRI knee osteoarthritis (OA) severity score, based on cartilage, osteophytes and meniscus (OA-COM), and to predict progression across different severity states using OA-COM as outcome and clinical variables as predictors. METHODS: Population-based knee pain cohort aged 40-79 was assessed at baseline and 7-year follow-up. OA-COM score was defined as the sum of MRI scores for cartilage, osteophytes and menisci, measured at 6, 8 and 6 sites, total score 0-54. To anchor severity levels, we fit cross-sectional logistic models using OA-COM to predict Kellgren-Lawrence (KL) grades in subsets at or one point below each grade. OA-COM threshold scores were selected on sensitivity, specificity, positive and negative predictive value. We developed longitudinal logistic models for OA-COM progression over each threshold over 7 years. Potential predictors included age, sex, BMI, malalignment, physical exam effusion, quadriceps weakness, and crepitus, selected on area under the receiver operating characteristic curve (AUC) and Akaike's Information Criterion (AIC). RESULTS: Optimal OA-COM thresholds were 12, 18, 24 and 30, for KL grades 1 to 4. Significant predictors of progression (depending on threshold) included physical exam effusion, malalignment and female sex, with other selected predictors age, BMI and crepitus. CONCLUSION: OA-COM (0-54 range) is a whole-joint, unidimensional, irreversible, and fine-grained MRI OA severity score reflecting cartilage, osteophytes and menisci. OA-COM scores 12, 18, 24 and 30 are equivalent to KL grades 1 to 4, while offering fine-grained differentiation of states between KL grades, and within pre-radiographic disease (KL = 0) or late-stage disease (KL = 4). In modeling, several clinical variables predicted progression across different states over 7 years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".