Autoantibodies against Endothelin 1 Type A Receptor Are Strong Predictors of Digital Ulcers in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To determine the predictive value of functional autoantibodies against vascular receptors for the development of ischemic digital ulcers (DU) in patients with systemic sclerosis (SSc). METHODS: Angiotensin II Type 1 receptor (AT1R) and endothelin 1 Type A receptor (ETAR) autoantibodies were measured at baseline in a prospective cohort of 90 patients with SSc together with 5 validated angiogenic markers. The primary outcome was the occurrence of at least 1 new ischemic DU during the 5-year followup. RESULTS: Twenty-four patients developed at least 1 new DU during the followup period. Univariate Cox analysis revealed that concentrations above the median value of anti-AT1R and anti-ETAR antibodies were predictive of the occurrence of ischemic DU (HR 2.85, 95% CI 1.19-6.84 and HR 3.39, 95% CI 1.35-8.50, respectively). A first multivariate Cox analysis including functional autoantibodies and clinical predictors of new DU confirmed anti-ETAR autoantibodies as independent predictors of the occurrence of new ischemic DU (HR 3.15, 95% CI 1.22-8.13) together with a history of DU at baseline. In a second model implemented with angiogenic markers, anti-ETAR autoantibodies remained an independent predictor of the occurrence of new ischemic DU (HR 9.59, 95% CI 1.75-52.64) together with the presence at baseline of active DU or history of DU. CONCLUSION: Anti-ETAR autoantibodies can be used together with the presence of current or past DU to identify patients with SSc who are at risk for the development of subsequent DU. These autoantibodies may allow for earlier management and therapeutic intervention.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".