Incident and prevalent cohorts with pulmonary arterial hypertension: insight from SERAPHIN
Bibliographic record
Abstract
In SERAPHIN, a long-term, randomised, controlled trial (NCT00660179) in pulmonary arterial hypertension (PAH), macitentan significantly reduced the risk of morbidity/mortality and PAH-related death/hospitalisation. We evaluated disease progression and the effect of macitentan in treatment-naïve incident and prevalent cohorts.Patients allocated to placebo, or macitentan 3 mg or 10 mg were classified by time from diagnosis to enrolment as incident (≤6 months; n=110) or prevalent (>6 months; n=157). The risk of morbidity/mortality and PAH-related death/hospitalisation was determined using Cox regression.The risk of morbidity/mortality (Kaplan-Meier estimates at month 12: 54.4% versus 26.7%; p=0.006) and PAH-related death/hospitalisation (Kaplan-Meier estimates at month 12: 47.3% versus 19.9%; p=0.006) were significantly higher for incident versus prevalent patients receiving placebo, respectively. There was no significant difference in the risk of all-cause death between incident and prevalent cohorts (p=0.587). Macitentan 10 mg significantly reduced the risk of morbidity/mortality and PAH-related death/hospitalisation versus placebo in incident and prevalent cohorts.Incident patients had a higher risk for PAH progression compared with prevalent patients but not a higher risk of death. Macitentan delayed disease progression in both incident and prevalent PAH patients.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".