Single-center prognostic validation of the risk assessment of the 2015 ESC/ERS guidelines in patients with pulmonary arterial hypertension in Japan
Bibliographic record
Abstract
The 2015 European Society of Cardiology/European Respiratory Society guidelines for the diagnosis and treatment of pulmonary hypertension include a multidimensional risk assessment for patients with pulmonary arterial hypertension (PAH). However, prognostic validations of this risk assessment are limited, especially outside Europe. Here, we validated the risk assessment strategy in PAH patients in our institution in Japan. Eighty consecutive PAH patients who underwent right heart catheterization between November 2006 and December 2018 were analyzed. Patients were classified as low, intermediate, or high risk by using a simplified version of the risk assessment that included seven variables: World Health Organization functional class, 6-min walking distance, peak oxygen consumption, brain natriuretic peptide, right atrial pressure, mixed venous oxygen saturation, and cardiac index. The high-risk group showed significantly higher mortality than the low- or intermediate-risk group at baseline (P < 0.001 for both comparisons), and the mortalities in the intermediate- and low-risk groups were both low (P = 0.989). At follow-up, patients who improved to or maintained a low-risk status showed better survival than those who did not (P = 0.041). Our data suggest that this risk assessment can predict higher mortality risk and long-term survival in PAH patients in Japan.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".