Moderated Posters session * New insights into risk stratification in valvular heart disease - Part A: 11/12/2013, 09:30-16:00 * Location: Moderated Poster area
Bibliographic record
Abstract
Background: The Society of Thoracic Surgeons Predicted Risk of Mortality (STS) score is used to risk stratify patients for transcatheter aortic valve replacement (TAVR).However, calculation of the STS score requires compilation of 24 parameters.We examined the prognostic value of: 1) STS score alone; 2) STS score with various transthoracic echocardiographic (TTE) parameters; and 3) TTE parameters without STS score in predicting mortality in patients with severe aortic stenosis (AS) ineligible for surgical valve replacement.Methods: Baseline demographics, TTE parameters, STS scores and clinical outcomes at an average follow-up of 1.8 years were obtained from 106 (40 male, 79+10 yrs) medicallymanaged inoperable patients with severe AS undergoing TTE from January 2007 to February 2012.TTE predictors of mortality were identified from univariate analyses.Hierarchical, multivariable Cox regression models were constructed.C-index was used to compare non-nested models.Results: Overall mortality rate was 56%.Left atrial volume (LA Vol), E/E', left ventricular ejection fraction (LVEF) and 4-chamber global longitudinal strain (4C-GLS) were significant univariable predictors of mortality.The combination of LA Vol, E/E', LVEF and 4C-GLS provided a better predictive model than the STS score alone or in combination with TEE parameters (Figure).Conclusions: Although STS score is an excellent predictor of mortality, the combination of LA Vol, E/E', LVEF and 4C-GLS is superior.This 4-parameter TTE score offers a practical and easier alternative to the STS score in predicting mortality in inoperable AS patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.513 | 0.212 |
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".