Validation of a Knowledge Transfer Tool According to the OMERACT Filter: Does Web-based Real-time Iterative Calibration Enhance the Evaluation of Bone Marrow Lesions in Hip Osteoarthritis?
Bibliographic record
Abstract
OBJECTIVE: To assess reliability and feasibility of using a Web-based interface and interactive online calibration tool for magnetic resonance imaging (MRI) scoring of bone marrow lesions (BML) in osteoarthritis (OA), applied to the Hip MR Inflammation Scoring System (HIMRISS). METHODS: Seven readers new to HIMRISS (3 radiologists, 4 rheumatologists) scored coronal short-tau inversion recovery MRI from a hip OA observational study obtained pre- and 8-week poststeroid injection (n = 40 × 2 scans × 2 hips = 160 hips). By crossover design, Group B (4 readers) scored 20 patients (40 hips) using conventional spreadsheet-based methods and then another 20 using a Web-based interface and an online real-time iterative calibration (RETIC) training module. Group A (3 readers) reversed the order, scoring the first 20 subjects by the new method and the final 20 conventionally. Outcomes included ICC and reader survey. RESULTS: Interobserver reliability for BML status was high by both spreadsheet and Web-based methods (0.84-0.90), regardless of the order in which scoring was performed. Reliability of change scores was moderate and improved with training. Improvement was greater in readers who began with the spreadsheet method and then used the Web-based method than in those who began with the Web-based method, especially at the acetabulum. Readers found Web-based/RETIC scoring more user-friendly and nearly 50% faster than traditional spreadsheet methods. CONCLUSION: HIMRISS offers reliable BML scoring in OA, whether by conventional spreadsheet-based scoring or by a Web-based interface with interactive feedback. The new method allowed faster readings, provided a consistent training environment that helped inexperienced readers achieve reliability equivalent to that of conventional methods, and was preferred by the readers.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".