Validation of a Novel Radiographic Scoring System for Calcinosis Affecting the Hands of Patients With Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: There are currently no validated outcome measures to assess calcinosis severity in systemic sclerosis (SSc; scleroderma). We sought to develop and validate a novel radiographic scoring system for calcinosis affecting the hands of SSc patients for potential use in future clinical trials. METHODS: Following a 1-hour teleconference training session, 12 investigators (8 rheumatologists, 1 dermatologist, and 3 radiologists) scored 12 hand radiographs in random order using 2 scoring systems (termed “simple” and “complex”) and re-scored 2 randomly assigned radiographs after a minimum of 24 hours. Interrater and intrarater reliability were assessed using a weighted kappa coefficient for the simple system (κ), and an intraclass correlation coefficient (ICC) for the complex system (ICC <0.4 for poor, 0.4–0.7 for moderate, and >0.7 for excellent). RESULTS: Mean time to complete the complex scoring system was significantly longer than the simple scoring system (4.0 versus 0.4 minutes; P < 0.0001). Overall interrater reliability for the simple scoring system was poor (κ = 0.39, 95% confidence interval [95% CI] 0.1–0.52) but improved if dichotomized as mild/moderate versus severe (κ = 0.51, 95% CI 0.26–0.7). Interrater reliability was excellent for the complex scoring system (ICC 0.89, 95% CI 0.86–0.92). Intrarater reliability was moderate for the simple scoring system (κ = 0.67, 95% CI 0.37–0.96) but almost perfect for the complex scoring system (ICC 0.93, 95% CI 0.89–0.97). CONCLUSION: We developed a novel radiographic scoring system that accounts for the area coverage, density, and anatomic location of calcinosis affecting the hands in patients with SSc. This scoring system is feasible with excellent reliability and should undergo further validation testing for use in clinical trials.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".