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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".