Predicting survival in pulmonary arterial hypertension: time to move forward
Bibliographic record
Abstract
In the early 1980s, the National Institutes of Health (NIH) established a prospective, multicenter registry to characterise the long-term outcomes of consecutive patients with primary pulmonary hypertension (PPH) 1, which included idiopathic, heritable and anorexigens-associated pulmonary arterial hypertension (PAH) 2. Another important objective of this registry was to determine variables predicting survival. This registry, initiated when no therapy for PAH was available, confirmed the dramatic prognosis of PPH on supportive therapy alone. Multiple variables associated with poor prognosis were documented, including poor functional capacity, low carbon monoxide diffusion capacity, presence of Raynaud phenomenon and pulmonary haemodynamics. From this, a formula that estimated a patient's chance of survival was developed, using three hemodynamic variables (right atrial and pulmonary artery pressures and cardiac index) chosen somewhat arbitrarily. This equation was subsequently validated in an independent cohort of patients with PPH 3. The demonstration of the devastating nature of PAH has justified resources for the development of new therapies, the prioritisation of PAH patients for lung transplantation 4 and the need for specialised PAH centres. Since the NIH registry was established, other variables have been increasingly recognised as potent prognostic factors in PAH, including PAH type, functional capacity and other surrogate markers of right heart function. With the advent of new treatments, a better prognostication certainly led to refinements in treatment approaches, with the most aggressive options being proposed to patients with more severe disease 2. Ultimately, the NIH equation has been useful to document higher survival on PAH-specific therapies …
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".