Bosentan in Clinical Practice for Treating Digital and Other Ischemic Ulcers in Spanish Patients with Systemic Sclerosis: IBER-DU Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To describe treatment outcomes and safety experience with bosentan in patients with systemic sclerosis (SSc) and digital ulcers (DU), in a clinical setting in Spain. METHODS: This was a multicenter, noninterventional retrospective cohort study. Data were collected retrospectively from patients with DU, with or without pulmonary arterial hypertension (PAH), who were initiating bosentan therapy in 2003 (n = 26) or 2004 (n = 41) and followed until May 2005. Data were obtained from centers prescribing bosentan. Relevant measures included number of DU, occurrence of new DU, overall DU clinical status (improved, stabilized, worsened), and bosentan-associated adverse events. RESULTS: Sixty-seven patients with SSc and DU or other ulcers were included. PAH was also present in 12 patients (18%). At the start of bosentan treatment, the median number of DU per patient was 3.0. The median change in number of DU was -3.6 and -5.0 at 12 and 24 months, respectively. Sixty-eight percent of the patients did not develop any new DU at 12 months. DU clinical status was reported at 12 months for 22 patients: 18 patients (81.8%) improved and 4 (18.2%) stabilized. The median treatment duration was 13.0 months. The main adverse event was increase of aminotransferase, observed in 5 patients (7%), leading to discontinuation of treatment in 3 patients (4.4%). CONCLUSION: Previously reported results of bosentan efficacy in DU management are reproducible in clinical practice. This efficacy is maintained in the longterm followup. Bosentan treatment was well tolerated and adverse events were comparable with those observed in previous reports.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".