Comparative validation of the knee inflammation MRI scoring system and the MRI osteoarthritis knee score for semi-quantitative assessment of bone marrow lesions and synovitis-effusion in osteoarthritis: an international multi-reader exercise
Bibliographic record
Abstract
Background: Bone marrow lesions (BMLs) and synovitis on magnetic resonance imaging (MRI) are associated with symptoms and predict degeneration of articular cartilage in osteoarthritis (OA). Validated methods for their semiquantitative assessment on MRI are available, but they all have similar scoring designs and questionable sensitivity to change. New scoring methods with completely different designs need to be developed and compared to existing methods. Objectives: To compare the performance of new web-based versions of the Knee Inflammation MRI Scoring System (KIMRISS) with the MRI OA Knee Score (MOAKS) for quantification of BMLs and synovitis-effusion (S-E). Design: Retrospective follow-up cohort. Methods: We designed web-based overlays outlining regions in the knee that are scored for BML in MOAKS and KIMRISS. For KIMRISS, both BML and S-E are scored on consecutive sagittal slices. The performance of these methods was compared in an international reading exercise of 8 readers evaluating 60 pairs of scans conducted 1 year apart from cases recruited to the OA Initiative (OAI) cohort. Interobserver reliability for baseline status and baseline to 1 year change in BML and S-E was assessed by intra-class correlation coefficient (ICC) and smallest detectable change (SDC). Feasibility was assessed using the System Usability Scale (SUS). Results: Mean change in BML and S-E was minimal over 1 year. Pre-specified targets for acceptable reliability (ICC ⩾ 0.80 and ⩾ 0.70 for status and change scores, respectively) were achieved more frequently for KIMRISS for both BML and synovitis. Mean (95% CI) ICC for change in BML was 0.88 (0.83-0.92) and 0.69 (0.60-0.78) for KIMRISS and MOAKS, respectively. KIMRISS mean SUS usability score was 85.7 and at the 95th centile of ranking for usability versus a score of 55.4 and 20th centile for MOAKS. Conclusion: KIMRISS had superior performance metrics to MOAKS for quantification of BML and S-E. Both methods should be further compared in trials of new therapies for OA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".