Efficacy of sildenafil on ischaemic digital ulcer healing in systemic sclerosis: the placebo-controlled SEDUCE study
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of sildenafil, a phosphodiesterase type 5 inhibitor, on digital ulcer (DU) healing in systemic sclerosis (SSc). METHODS: Randomised, placebo-controlled study in patients with SSc to assess the effect of sildenafil 20 mg or placebo, three times daily for 12 weeks, on ischaemic DU healing. The primary end point was the time to healing for each DU. Time to healing was compared between groups using Cox models for clustered data (two-sided tests, p=0.05). RESULTS: Intention-to-treat analysis involved 83 patients with a total of 192 DUs (89 in the sildenafil group and 103 in the placebo group). The HR for DU healing was 1.33 (0.88 to 2.00) (p=0.18) and 1.27 (0.85 to 1.89) (p=0.25) when adjusted for the number of DUs at entry, in favour of sildenafil. In the per protocol population, the HRs were 1.49 (0.98 to 2.28) (p=0.06) and 1.43 (0.93 to 2.19) p=0.10. The mean number of DUs per patient was lower in the sildenafil group compared with the placebo group at week (W) 8 (1.23±1.61 vs 1.79±2.40 p=0.04) and W12 (0.86±1.62 vs 1.51±2.68, p=0.01) resulting from a greater healing rate (p=0.01 at W8 and p=0.03 at W12). CONCLUSIONS: The primary end point was not reached in intention-to-treat, partly because of an unexpectedly high healing rate in the placebo group. We found a significant decrease in the number of DUs in favour of sildenafil compared with placebo at W8 and W12, confirming a sildenafil benefit. TRIAL REGISTRATION NUMBER: NCT01295736.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".