Possible role of MRI-detected osteophytes as a predictive biomarker for development of osteoarthritis of the knee: A study using data from the Osteoarthritis Initiative
Bibliographic record
Abstract
Objective: To elucidate the possible role of MRI-detected osteophytes as a predictive imaging biomarker for knee osteoarthritis (KOA). Design: Subjects (n = 303) were selected according to the following inclusion criteria from the Osteoarthritis Initiative (OAI) data set: (1) < 55 years old; (2) Western Ontario and McMaster Universities Arthritis Index pain score of 0; (3) Kellgren-Lawrence (KL) system grade 0 or 1; and (4) Complete MRI data set of the right knee. A pre-OA group (POA) consisted of subjects who developed KL grade 2 or more within 96 months, and a non-OA group (NOA) that remained KL 0 or 1 during that period. Baseline MRIs were assessed for osteophyte formation. Twenty-five locations were examined according to the MOAKS osteophyte score. Osteophytes at each location were assessed in terms of their predictive value for OA development. Results: Thirty-two subjects were POA and 271 were NOA. Age, BMI, and sex did not differ between the two groups. In the POA group, the number of subjects with osteophytes tended to be higher at all 25 sites. Forward stepwise regression analysis revealed five locations - medial patella, lateral intra-condylar notch of the femur, lateral femoral condyle, tibial spine, and lateral posterior condyle - were important for the prediction of KOA development. Having more than two osteophytes at these five locations predicted KOA development with a sensitivity of 0.75 and specificity of 0.79. Conclusions: MRI-detected osteophytes could serve as a predictive biomarker of KOA development within 96 months after detection.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".