Validation of a Knowledge Transfer Tool for the Knee Inflammation MRI Scoring System for Bone Marrow Lesions According to the OMERACT Filter: Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To assess feasibility and reliability of scoring bone marrow lesions (BML) on knee magnetic resonance imaging (MRI) in osteoarthritis using the Outcome Measures in Rheumatology Knee Inflammation MRI Scoring System (KIMRISS), with a Web-based interface and online training with real-time iterative calibration. METHODS: Six readers new to the KIMRISS (3 radiologists, 3 rheumatologists) scored sagittal T2-weighted fat-saturated MRI in 20 subjects randomly selected from the Osteoarthritis Initiative data, at baseline and 1-year followup. In the KIMRISS, the reader moves a transparent overlay grid within a Web-based interface to fit bones, then clicks or touches each region containing BML per slice, to score 1 if BML is present. Regional and total scores are automatically calculated. Outcomes include the interreader intraclass correlation coefficients (ICC) and the smallest detectable change (SDC). RESULTS: Scoring took 3-12 min per scan and all readers rated the process as moderately to very user friendly. Despite a low BML burden (average score 2.8% of maximum possible) and small changes, interobserver reliability was moderate to high for BML status and change in the femur and tibia (ICC 0.78-0.88). Four readers also scored the patella reliably, whereas 2 readers were outliers, likely because of image artifacts. SDC of 1.5-5.6 represented 0.7% of the maximum possible score. CONCLUSION: We confirmed feasibility of knee BML scoring by new readers using interactive training and a Web-based touch-sensitive overlay system, finding high reliability and sensitivity to change. Further work will include adjustments to training materials regarding patellar scoring, and study in therapeutic trial datasets with higher burden of BML and larger changes.
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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 | 0.040 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".