Staging classification of aortic stenosis based on the extent of cardiac damage
Bibliographic record
Abstract
AIMS: In patients with aortic stenosis (AS), risk stratification for aortic valve replacement (AVR) relies mainly on valve-related factors, symptoms and co-morbidities. We sought to evaluate the prognostic impact of a newly-defined staging classification characterizing the extent of extravalvular (extra-aortic valve) cardiac damage among patients with severe AS undergoing AVR. METHODS AND RESULTS: Patients with severe AS from the PARTNER 2 trials were pooled and classified according to the presence or absence of cardiac damage as detected by echocardiography prior to AVR: no extravalvular cardiac damage (Stage 0), left ventricular damage (Stage 1), left atrial or mitral valve damage (Stage 2), pulmonary vasculature or tricuspid valve damage (Stage 3), or right ventricular damage (Stage 4). One-year outcomes were compared using Kaplan-Meier techniques and multivariable Cox proportional hazards models were used to identify 1-year predictors of mortality. In 1661 patients with sufficient echocardiographic data to allow staging, 47 (2.8%) patients were classified as Stage 0, 212 (12.8%) as Stage 1, 844 (50.8%) as Stage 2, 413 (24.9%) as Stage 3, and 145 (8.7%) as Stage 4. One-year mortality was 4.4% in Stage 0, 9.2% in Stage 1, 14.4% in Stage 2, 21.3% in Stage 3, and 24.5% in Stage 4 (Ptrend < 0.0001). The extent of cardiac damage was independently associated with increased mortality after AVR (HR 1.46 per each increment in stage, 95% confidence interval 1.27-1.67, P < 0.0001). CONCLUSION: This newly described staging classification objectively characterizes the extent of cardiac damage associated with AS and has important prognostic implications for clinical outcomes after AVR.
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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 | 0.005 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".