Risk stratification in pulmonary arterial hypertension
Bibliographic record
Abstract
PURPOSE OF REVIEW: Periodic risk stratification is recommended for patients with pulmonary arterial hypertension (PAH). The purpose of this article is to review the available risk stratification tools in PAH. RECENT FINDINGS: Validated tools, such as the REVEAL (Registry to Evaluate Early And Long-term PAH Disease Management) score or European Society of Cardiology/European Respiratory Society (ESC/ERS) risk assessment table incorporate clinical, exercise, imaging, and hemodynamic variables to obtain a multidimensional estimate of prognosis. Recent studies from REVEAL and European registries have further validated these tools and improved our understanding of prognostic factors in PAH. Serial risk assessment over time, especially after treatment initiation or modification, provides more useful information than at a single time point, with changes in risk profiles being particularly informative. A brief, simplified approach includes counting the number of ESC/ERS low-risk criteria achieved, with 3 or 4 low-risk criteria associated with an excellent prognosis. When a more precise estimation of risk is desired, the REVEAL score can delineate five risk groups with 1-year survival rates between 97.3% (low risk) and 50.2% (very high risk). SUMMARY: The REVEAL and ESC/ERS risk table are useful, validated, multidimensional risk stratification tools that should be periodically applied to patients with PAH in practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.004 | 0.001 |
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".