P6342Risk assessment according to the 2015 ESC guidelines risk prediction model of patients with chronic thromboembolic pulmonary hypertension (CTEPH)
Bibliographic record
Abstract
Introduction: Pulmonary endarterectomy (PEA) is the treatment of choice for patients with chronic thromboembolic pulmonary hypertension (CTEPH). However, data on the long-term course after PEA are limited and strategies for risk assessment have not been investigated in CTEPH patients thus far. Purpose: The aim of the present study was to investigate whether the ESC 2015 guidelines risk prediction model developed for PAH patients allows risk stratification of CTEPH patients after PEA. Methods: CTEPH patients treated with PEA in an experienced German centre between January 2014 and December 2015 were included in the present study. The ESC 2015 guidelines risk prediction model was used to classify patients into low- (0–4 points), intermediate- (5–8 points) and high- (9–16 points) risk of 1-year mortality combining information from clinical, laboratory, exercise and haemodynamic examinations obtained prior PEA. For eight variables available, 0 to 2 points were given each using recommended cut-off values. Results: Of 237 CTEPH patients who underwent PEA, 230 (97.0%; 46.1% female, median age 64 [IQR 52–72] years) with complete 1-year follow-up were included in the present analysis. During the first year after PEA, 12 (5.2%) patients died (median time to death, 29 [14–228] days).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".