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 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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".