External validation of a refined four-stratum risk assessment score from the French pulmonary hypertension registry
Bibliographic record
Abstract
INTRODUCTION: Contemporary risk assessment tools categorise patients with pulmonary arterial hypertension (PAH) as low, intermediate or high risk. A minority of patients achieve low risk status with most remaining intermediate risk. Our aim was to validate a four-stratum risk assessment approach categorising patients as low, intermediate-low, intermediate-high or high risk, as proposed by the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) investigators. METHODS: We evaluated incident patients from the French PAH Registry and applied a four-stratum risk method at baseline and at first reassessment. We applied refined cut-points for three variables: World Health Organization functional class, 6-min walk distance and N-terminal pro-brain natriuretic peptide. We used Kaplan-Meier survival analyses and Cox proportional hazards regression to assess survival according to three-stratum and four-stratum risk approaches. RESULTS: At baseline (n=2879), the four-stratum approach identified four distinct risk groups and performed slightly better than a three-stratum method for predicting mortality. Four-stratum model discrimination was significantly higher than the three-stratum method when applied during follow-up and refined risk categories among subgroups with idiopathic PAH, connective tissue disease-associated PAH, congenital heart disease and portopulmonary hypertension. Using the four-stratum approach, 53% of patients changed risk category from baseline compared to 39% of patients when applying the three-stratum approach. Those who achieved or maintained a low risk status had the best survival, whereas there were more nuanced differences in survival for patients who were intermediate-low and intermediate-high risk. CONCLUSIONS: The four-stratum risk assessment method refined risk prediction, especially within the intermediate risk category of patients, performed better at predicting survival and was more sensitive to change than the three-stratum approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".