Reproducibility of capillaroscopic classifications of systemic sclerosis: results from the SCLEROCAP study
Bibliographic record
Abstract
Objectives: Subgroups of capillaroscopic scleroderma landscape have been correlated with stages of SSc: two groups for Maricq's classification (slow and active), and three for Cutolo's classification (early, active and late). We report inter- and intra-observer agreement for these classifications as a preliminary step in the multicentre prospective SCLEROCAP study, which aims to assess the classification and single capillaroscopic items as prognostic tools for SSc. Methods: SCLEROCAP included 385 patients. Agreement was studied in the first 100 patients, who were independently rated twice by two observers, blind to patients' characteristics; 30 of the patients were rated once by six observers. After consensus meetings, these ratings were held again. Kappa and intraclass correlation coefficients were used to assess agreement. Results: Interobserver agreement on 100 patients was moderate for Maricq and Cutolo classifications [κ 0.47 (0.28, 0.66) and 0.49 (0.33, 0.65), respectively], and became substantial after consensus meetings [0.64 (0.50, 0.77) and 0.69 (0.56, 0.81)]. Intra-observer agreement between two observers was moderate to substantial: κ 0.54 (0.33, 0.75) and 0.70 (0.57, 0.83) for Maricq's classification; 0.57 (0.38, 0.77) and 0.76 (0.65, 0.87) for Cutolo's. Thirty patients were rated once by each of six observers, and agreement was moderate to substantial: κ 0.57 ± 0.10 (Maricq) and 0.61 ± 0.12 (Cutolo). Agreement was substantial for bushy, giant capillaries and microhaemorrhages, moderate for capillary density and low for oedema, disorganization and avascular areas. Conclusion: The moderate reproducibility of Maricq and Cutolo classifications might hamper their prognostic value in SSc patients. Consensus meetings improve reliability, a prerequisite for better prognostic performances. A focus on giant capillaries, haemorrhages and capillary density might be more reliable.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".