Defining hip cartilage repair: a modified delphi study to establish the Magnetic Resonance Evaluation of the Repair of Cartilage in the Hip (MERCH) score
Bibliographic record
Abstract
Abstract Purpose To develop a standardized scoring system to evaluate pre- to post-operative repair or reconstruction of hip cartilage using magnetic resonance imaging (MRI). Methods A two-phase modified Delphi study was conducted. Phase 1 involved a survey with suggested criteria and diagrams to define various stages of articular cartilage repair and phase 2 involved an expert consensus meeting that discussed the survey responses and voted on final scoring criteria. The survey was emailed to members of the Canadian Hip Preservation Research Collaborative (CHIPR) and respondents included both board certified orthopedic surgeons as well as musculoskeletal radiologists. Results Overall, there were 17 survey respondents from Canada and most (47%, 8/17) participants agreed that the minimum MRI protocol needed to evaluate cartilage repair was a 3.0 T MRI and 94% (17/18) agreed that the minimum time post-operatively that they felt they would be able to accurately evaluate cartilage repair on an MRI was 12 months. Following phases 1 and 2, the final Magnetic Resonance Evaluation of the Repair of Cartilage in the Hip (MERCH) score was developed with 7 domains, 3 criteria per domain: 1) volume fill of cartilage defect, 2) integration into adjacent cartilage, 3) surface of the repair tissue, 4) structure of the repair tissue, 5) bony overgrowth, 6) subchondral changes, and 7) delamination. The score ranges from 60 (optimal) to -20 points (worst/none). Conclusions This consensus project established a new MRI scoring system to evaluate post-operative cartilage restoration of the hip. The implementation of the MERCH score is essential in our ability to guide patient management and expectations in a rapidly evolving field and will help with standardizing our evaluation of cartilage repair in future research trials. Level of Evidence Level II Diagnostic.
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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 | 0.107 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".