Preliminary validation of the Knee Inflammation MRI Scoring System (KIMRISS) for grading bone marrow lesions in osteoarthritis of the knee: data from the Osteoarthritis Initiative
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Bone marrow lesions (BML) are an MRI feature of osteoarthritis (OA) offering a potential target for therapy. We developed the Knee Inflammation MRI Scoring System (KIMRISS) to semiquantitatively score BML with high sensitivity to small changes, and compared feasibility, reliability and responsiveness versus the established MRI Osteoarthritis Knee Score (MOAKS). METHODS: KIMRISS incorporates a web-based graphic overlay to facilitate detailed regional BML scoring. Observers scored BML by MOAKS and KIMRISS on sagittal fluid-sensitive sequences. Exercise 1 focused on interobserver reliability in Osteoarthritis Initiative observational data, with 4 readers (two experienced/two new to KIMRISS) scoring BML in 80 patients (baseline/1 year). Exercise 2 focused on responsiveness in an open-label trial of adalimumab, with 2 experienced readers scoring BML in 16 patients (baseline/12 weeks). RESULTS: Scoring time was similar for KIMRISS and MOAKS. Interobserver reliability of KIMRISS was equivalent to MOAKS for BML status (ICC=0.84 vs 0.79), but consistently better than MOAKS for change in BML: Exercise 1 (ICC 0.82 vs 0.53), Exercise 2 (ICC 0.90 vs 0.32), and in new readers (0.87-0.92 vs 0.32-0.51). KIMRISS BML was more responsive than MOAKS BML: post-treatment BML improvement in Exercise 2 reached statistical significance for KIMRISS (SRM -0.69, p=0.015), but not MOAKS (SRM -0.12, p=0.625). KIMRISS BML also more strongly correlated to WOMAC scores than MOAKS BML (r=0.80 vs 0.58, p<0.05). CONCLUSIONS: KIMRISS BML scoring was highly feasible, and was more reliable for assessment of change and more responsive to change than MOAKS BML for expert and new readers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it