Evaluation of Nailfold Capillaroscopy Online Training Using the Fast Track Algorithm
Bibliographic record
Abstract
OBJECTIVE: Nailfold capillaroscopy (NFC) is increasingly used in the early identification of systemic sclerosis (SSc)-related disorders. A consensus "Fast Track algorithm" was developed by the European Alliance of Associations for Rheumatology to aid differentiation of scleroderma from nonscleroderma pattern on NFC. Our objective was to evaluate the online training of NFC using the Fast Track algorithm in the assessment of scleroderma vs nonscleroderma NFC pattern. METHODS: Participants attended the NFC online training workshop and were taught the Fast Track algorithm. Following the training, participants independently evaluated 45 NFC images in the same session, and then 2 to 4 weeks later, through the online platform. Participants had to differentiate between scleroderma vs nonscleroderma pattern, and additionally nonscleroderma pattern (normal) vs nonscleroderma pattern (nonspecific). The inter- and intrarater Cohen [Formula: see text] agreement was calculated. RESULTS: Ninety-eight participants took part in the baseline evaluation, and 61 in the reevaluation session. For identification of scleroderma vs nonscleroderma pattern, the mean (95% CI) inter- and intrarater [Formula: see text] were 0.86 (0.83-0.88) and 0.83 (0.79-0.87), respectively. The overall inter- and intrarater [Formula: see text] in the identification of scleroderma, nonscleroderma (normal), and nonscleroderma (nonspecific) patterns were 0.71 (0.69-0.74) and 0.71 (0.67-0.75), respectively. For nonscleroderma (normal) vs nonscleroderma (nonspecific) pattern, the inter- and intrarater [Formula: see text] were 0.59 (0.55-0.63) and 0.59 (0.54-0.65), respectively. CONCLUSION: In this first study evaluating NFC online training using the Fast Track algorithm, we showed very good inter- and intrarater agreement for the identification of scleroderma and nonscleroderma NFC pattern, supporting the feasibility of online NFC standardized training workshops.
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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 | 0.011 | 0.024 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".