Relationship between calcium channel blockers and skin fibrosis in patients with systemic sclerosis.
Bibliographic record
Abstract
OBJECTIVES: Recent experimental evidence suggests that calcium channel blockers (CCBs) may have anti-fibrotic effects on liver and pulmonary fibrosis. We aimed to investigate whether use of CCBs was associated with the skin fibrosis in patients with systemic sclerosis (SSc). METHODS: Based on the 5-year follow-up data from the Canadian Scleroderma Research Group registry, we used the generalised estimating equations (GEE) model to assess the relationship between use of CCBs and the primary outcome of skin fibrosis measured by the modified Rodnan skin score (mRSS). We also used GEE models to explore the associations between use of CCBs and risk of secondary outcomes including digital ulcers, pulmonary fibrosis, calcinosis, and scleroderma renal crisis. RESULTS: There were 1547 patients (1330 females) with SSc included in this study. Their mean age was 55.5 years and there were 606 patients taking CCBs at baseline. No significant difference in mRSS between the use versus non-use of CCBs was found in the multivariable analysis: mean difference = -0.19 (95% confidence interval: -0.62, 0.23), p-value = 0.37. Use of CCBs was not significantly related to risk of secondary outcomes, with an odds ratio (OR) of 1.13 for digital ulcers, 0.94 for pulmonary fibrosis, 0.90 for calcinosis and 1.69 for scleroderma renal crisis, respectively. CONCLUSIONS: No significant associations between use of CCBs and skin fibrosis, digital ulcers, pulmonary fibrosis, calcinosis and scleroderma renal crisis were found in patients with SSc. More evidence from other well-designed studies would be required to confirm these findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".