Calcinosis is associated with digital ischaemia in systemic sclerosis—a longitudinal study
Bibliographic record
Abstract
OBJECTIVE: To determine if ischaemia is a causal factor in the development of calcinosis in SSc. METHODS: Patients with SSc were assessed yearly. Physicians reported the presence of calcinosis, digital ischaemia (digital ulcers, digital necrosis/gangrene, loss of digital pulp on any digits and/or auto- or surgical digital amputation) and nailfold capillary dropout assessed using a dermatoscope. The number of digits with digital ischaemia was used as an assessment of the severity of digital ischaemia. SSc specific antibodies were detected with a line immunoassay. Multiple logistic regression and Cox proportional hazards models were generated to determine associations between calcinosis, digital ischaemia and capillary dropout. RESULTS: One thousand three hundred and five patients were included in this study, of whom 300 (23.0%) had calcinosis at study entry. In a cross-sectional multivariate analysis, at baseline, calcinosis was associated with digital ischaemia (odds ratio (OR) = 2.37, 95% CI: 1.66, 3.39), severity of ischaemia (OR = 1.12, 95% CI: 1.06, 1.18), capillary dropout (OR = 1.41, 95% CI: 1.05, 1.89), ACAs (OR = 1.68, 95% CI: 1.17, 2.43) and anti-RNA polymerase III antibodies (OR = 1.77, 95% CI: 1.08, 2.89). Current use of calcium channel blockers was inversely associated with the presence of calcinosis (OR = 0.70, 95% CI: 0.52, 0.96). Of the 805 patients with no calcinosis at study entry and at least one follow-up visit, 215 (26.7%) developed calcinosis during follow-up. Significant baseline predictors of the development of calcinosis in follow-up were digital ischaemia (hazard ratio (HR) = 1.82, 95% CI: 1.30, 2.54), capillary dropout (HR = 1.46, 95% CI: 1.08, 1.99), dcSSc (HR = 1.57, 95% CI: 1.11, 2.21), ACA (HR = 2.18, 95% CI: 1.50, 3.17) and anti-RNA polymerase III antibodies (HR = 2.58, 95% CI: 1.65, 4.04). CONCLUSION: Ischaemia may play a role in the development of calcinosis in SSc.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".