Warfarin in Systemic Sclerosis-associated and Idiopathic Pulmonary Arterial Hypertension. A Bayesian Approach to Evaluating Treatment for Uncommon Disease
Bibliographic record
Abstract
OBJECTIVE: Warfarin is recommended in systemic sclerosis-associated pulmonary arterial hypertension (SSc-PAH) and idiopathic PAH (IPAH) to improve survival. There is no evidence to support this in SSc-PAH and the evidence in IPAH is conflicting. We evaluated the ability of warfarin to improve survival using 2 large SSc-PAH and IPAH cohorts. METHODS: The effect of warfarin on all-cause mortality was evaluated. Bayesian propensity scores (PS) were used to adjust for baseline differences between patients exposed and not exposed to warfarin, and to assemble a matched cohort. Bayesian Cox proportional hazards models were constructed using informative priors based on international PAH expert elicitation. RESULTS: Review of 1138 charts identified 275 patients with SSc-PAH (n = 78; 28% treated with warfarin) and 155 patients with IPAH (n = 91; 59% treated with warfarin). Baseline differences in PAH severity and medications were resolved using PS matching. In the matched cohort of 98 patients with SSc-PAH (49 treated with warfarin), the posterior median hazard ratio (HR) was 1.06 [95% credible interval (CrI) 0.70, 1.63]. In the matched cohort of 66 patients with IPAH (33 treated with warfarin), the posterior median HR was 1.07 (95% CrI 0.57, 1.98). The probability that warfarin improves median survival by 6 months or more is 23.5% in SSc-PAH and 27.7% in IPAH. Conversely, there is a > 70% probability that warfarin provides no significant benefit or is harmful. CONCLUSION: There is a low probability that warfarin improves survival in SSc-PAH and IPAH. Given the availability of other PAH therapies with demonstrable benefits, there is little reason to use warfarin to improve survival for these patients.
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 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 | 0.026 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".