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 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 | 0.022 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".