The effect of calcium channel blockers on digital ulcers in systemic sclerosis: data from a prospective cohort study
Bibliographic record
Abstract
Abstract Digital ulcers (DU) are a common, severe vascular manifestation of systemic sclerosis (SSc) with few effective treatment options. Using data from the Australian Scleroderma Cohort Study (ASCS), we sought to evaluate the effect of calcium channel blockers (CCB) on the treatment and prevention of DU. Using data from 1953 participants, with a median of 4.34 years of follow-up, we used generalised estimating equations to evaluate the clinical characteristics associated with CCB use and ascertain the risk factors for the presence of DU at subsequent study visits. A time-dependent Cox-proportional hazard model was applied to evaluate the risk of future occurrence of DU with CCB use. Sixty-six percent of participants received CCB and patients with a history of DU were more likely to be prescribed a CCB (76.76% vs 53.70%, p < 0.01). CCB use was more frequent in patients with severe complications of DU including chronic DU (OR 1.47, p = 0.02), need for hospitalisation for iloprost (OR 1.30, p = 0.01) or antibiotics (OR 1.36, p = 0.04) and digital amputation (OR 1.48, p < 0.01). Use of CCB was more likely in patients who experienced DU at subsequent study visits (OR 1.32, p < 0.01) and was not associated with a decreased risk of the development of a first DU (HR 0.94, p = 0.65). CCB are frequently used in the management of SSc in the ASCS and their use is associated with severe peripheral vascular manifestations of SSc. However, our results suggest that CCB may not be effective in the healing or prevention of DU. Key Points • Calcium channel blockers (CCB) are commonly used in patients with vascular manifestations of systemic sclerosis (SSc). • CCB did not reduce the risk of the development of the first episode of digital ulcers when used prior to the onset of SSc digital ulcers. • CCB use was not associated with a reduction in the rate of digital ulcer recurrence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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 | 0.001 | 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".