Preliminary Validation of 2 Magnetic Resonance Image Scoring Systems for Osteoarthritis of the Hip According to the OMERACT Filter
Bibliographic record
Abstract
OBJECTIVE: Development of a validated magnetic resonance image (MRI) scoring system is essential in hip OA because radiographs are insensitive to change. We assessed the feasibility and reliability of 2 previously developed scoring methods: (1) the Hip Inflammation MRI Scoring System (HIMRISS) and (2) the Hip Osteoarthritis MRI Scoring System (HOAMS). METHODS: Six readers (3 radiologists, 3 rheumatologists) participated in 2 reading exercises. In Reading Exercise 1, MRI of the hip of 20 subjects were read at a single time point followed by further standardization of methodology. In Reading Exercise 2, MRI of the hip of 18 subjects from a randomized controlled trial, assessed at 2 timepoints, and 27 subjects from a cross-sectional study were read for HIMRISS and HOAMS bone marrow lesions (BML) and synovitis. Reliability was assessed using intraclass correlation coefficient (ICC) and kappa statistics. RESULTS: Both methods were considered feasible. For Reading 1, HIMRISS ICC were 0.52, 0.61, 0.70, and 0.58 for femoral BML, acetabular BML, effusion, and total scores, respectively; and for HOAMS, summed BML and synovitis ICC were 0.52 and 0.46, respectively. For Reading 2, HIMRISS and HOAMS ICC for BML and synovitis-effusion improved substantially. Interobserver reliability for change scores was 0.81 and 0.71 for HIMRISS femoral and HOAMS summed BML, respectively. Responsiveness and discrimination was moderate to high for synovitis-effusion. Significant associations were noted between BML or synovitis scores and Western Ontario and McMaster Universities Osteoarthritis Index pain scores for baseline values (p ≤ 0.001). CONCLUSION: The BML and synovitis-effusion components of both HIMRISS and HOAMS scoring systems are feasible and reliable, and should be validated further.
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 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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".