Nailfold Capillaroscopy for Prediction of Novel Future Severe Organ Involvement in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Assessment of associations of nailfold videocapillaroscopy (NVC) scleroderma (systemic sclerosis; SSc) ("early," "active," and "late") with novel future severe clinical involvement in 2 independent cohorts. METHODS: Sixty-six consecutive Belgian and 82 Italian patients with SSc underwent NVC at baseline. Images were blindly assessed and classified into normal, early, active, or late NVC pattern. Clinical evaluation was performed for 9 organ systems (general, peripheral vascular, skin, joint, muscle, gastrointestinal tract, lung, heart, and kidney) according to the Medsger disease severity scale (DSS) at baseline and in the future (18-24 months of followup). Severe clinical involvement was defined as category 2 to 4 per organ of the DSS. Logistic regression analysis (continuous NVC predictor variable) was performed. RESULTS: The OR to develop novel future severe organ involvement was stronger according to more severe NVC patterns and similar in both cohorts. In simple logistic regression analysis the OR in the Belgian/Italian cohort was 2.16 (95% CI 1.19-4.47, p = 0.010)/2.33 (95% CI 1.36-4.22, p = 0.002) for the early NVC SSc pattern, 4.68/5.42 for the active pattern, and 10.14/12.63 for the late pattern versus the normal pattern. In multiple logistic regression analysis, adjusting for disease duration, subset, and vasoactive medication, the OR was 2.99 (95% CI 1.31-8.82, p = 0.007)/1.88 (95% CI 1.00-3.71, p = 0.050) for the early NVC SSc pattern, 8.93/3.54 for the active pattern, and 26.69/6.66 for the late pattern versus the normal pattern. CONCLUSION: Capillaroscopy may be predictive of novel future severe organ involvement in SSc, as attested by 2 independent cohorts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".