International consensus on a proposed score system for muscle biopsy evaluation in patients with juvenile dermatomyositis: A tool for potential use in clinical trials
Bibliographic record
Abstract
OBJECTIVE: To devise and test a system with which to evaluate abnormalities on muscle biopsy samples obtained from children diagnosed with juvenile dermatomyositis (DM). METHODS: We established an International Consensus Group on Juvenile DM Biopsy and carried out 2 phases of consensus process and scoring workshops. Biopsy sections (n = 33) were stained by standard methods. The scoring tool was based on 4 domains of change: inflammatory, vascular, muscle fiber, and connective tissue. Using a Latin square design, biopsy samples were scored by 11 experts for items in each domain, and for a global abnormality measure using a 10-cm visual analog score (VAS 0-10). The tool's reliability was assessed using an intraclass correlation coefficient (ICC) and scorer agreement (alpha) by determining variation in scorers' ratings. RESULTS: There was good agreement in many items of the tool, and several items refined between the meetings improved in reliability and/or agreement. The inflammatory and muscle fiber domains had the highest reliability and agreement. The overall VAS score for abnormality had high agreement and reliability, reaching an ICC of 0.863 at the second consensus meeting. CONCLUSION: We propose a provisional scoring system to measure abnormalities on muscle biopsy samples obtained from children with juvenile DM. This system needs to be validated, and then could be used in prospective studies to test which features of muscle pathology are prognostic of disease course or outcome. We suggest that the process we used could be a template for developing similar systems in other forms of myositis.
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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 | 0.266 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".