Correlation between capillaroscopic classifications and severity in systemic sclerosis: results from SCLEROCAP study at inclusion.
Bibliographic record
Abstract
OBJECTIVES: We assessed the correlation between severity of systemic sclerosis (SSc) and current staging systems based on nailfold capillaroscopy. METHODS: SCLEROCAP is a multicenter prospective study including consecutive scleroderma patients who have a yearly routine follow-up with capillaroscopy and digital blood pressure measurement. Capillaroscopy images were read by two observers blinded from each other, then by a third one in the case of discordance. A follow-up of 3 years is planned. The present study assessed the correlation between severity of systemic sclerosis (SSc) and current staging systems based on nail fold capillaroscopy at enrollment in the SCLEROCAP study. Univariate and multivariate logistic regression analysis was performed for both the Maricq and Cutolo classifications. RESULTS: SCLEROCAP included 387 patients in one year. Maricq's active and Cutolo's late classifications were very similar. In multivariate analysis, the number of digital ulcers (OR for 2 ulcers or more, respectively 2.023 [1.074-3.81] and 2.596 [1.434-4.699]) and Rodnan's skin score >15 (OR respectively 32.007 [6.457-158.658] and 18.390 [5.380-62.865]) correlated with Maricq's active and Cutolo's late stages. Haemoglobin rate correlated with Cutolo's late stage (hemoglobin<100 vs. >120 g/dl: OR 0.223 [0.051-0.980]), and total lung capacity with Maricq's active one: increase in 10%: OR0.833 [0.717-0.969]. CONCLUSIONS: The correlations found between capillaroscopy and severity of SSc are promising before the ongoing prospective study definitively assesses whether capillaroscopy staging predicts complications of SSc. Only two capillaroscopic patterns seem useful: one involving many giant capillaries and haemorrhages and the other with severe capillary loss.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".