Effect of Warfarin on Survival in Scleroderma-associated Pulmonary Arterial Hypertension (SSc-PAH) and Idiopathic PAH. Belief Elicitation for Bayesian Priors
Bibliographic record
Abstract
OBJECTIVE: Warfarin use in scleroderma (SSc)-associated pulmonary arterial hypertension (PAH) and idiopathic PAH (IPAH) is controversial. A prerequisite for a trial is the demonstration of community uncertainty. We evaluated experts' beliefs about the effect of warfarin on 3-year survival in SSc-PAH and IPAH, and factors that influence warfarin use. METHODS: PAH experts attending the 2008 American College of Rheumatology or American Thoracic Society meetings expressed the probability of 3-year survival without and with warfarin and their degree of uncertainty by applying adhesive dots, each representing a 5% weight of probability, in "bins" on a line, creating a prior probability distribution or prior. Using a numeric rating scale, participants rated factors that influence their use of warfarin. RESULTS: Forty-five experts (44% pulmonologists, 38% rheumatologists, 16% cardiologists, 2% internists) underwent the belief elicitation interview. In SSc-PAH, the mean probabilities of 3-year survival without and with warfarin were 54% and 56%, respectively. Pessimistic experts believe that warfarin worsens survival by 7%. Optimistic experts believe that warfarin improves survival by 13%. In IPAH, the mean probabilities of 3-year survival without and with warfarin were 68% and 76%. Factors (mean rating out of 10, 0 = not at all important, 10 = extremely important) that influence experts' use of warfarin were functional class (5.4), age (5.4), pulmonary artery pressure (5.2), peripheral vascular disease (3.6), disease duration (2.8), and sex (1.7). CONCLUSION: Bayesian priors effectively quantify and illustrate experts' beliefs about the effect of warfarin on survival in SSc-PAH and IPAH. This study demonstrates the presence of uncertainty about the effect of warfarin, and provides justification for a clinical trial.
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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 | 0.001 |
| 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".