Comparison of 5 Risk Scales in the Results of Aortic Valvular Surgery With Rapid Deployment Prostheses
Bibliographic record
Abstract
Objective: Aortic prostheses with rapid deployment have recently emerged with shorter surgical times and less invasive approaches for the treatment of aortic stenosis. The appearance of the treatment via TAVR has caused the calculation of the preoperative risk to become more interesting to select the right treatment. We studied the prognostic utility of 5 surgical risk scales (Euroscore logistic, EuroScore II, STSAVR score, NNEAVRscore and OntarioScore) to detect mortality in a cohort of patients who received rapid deployment aortic prostheses. Methods: From September 2012 to November 2016 we reviewed the patients who received Edwards Intuity prostheses. We calculated 5 risk scales assessing their effectiveness in hospital mortality. Results: Seventy-two patients (68% males, 75.6 ± 4.8 years). Two patients died (2.8%). The mean values of the Euroscore logistic, EuroScore 2, STSAVRScore, NNEAVRScore and OntarioScore were: 7.7 ± 4.9; 2.6 ± 1.9; 3 ± 1.7; 3.5 ± 2.6 and 5.5 ± 1.3 respectively. For mortality discrimination the results were: EuroScore logistic: 0.92 (95% CI 0.83-1), EuroScore 2: 0.83 (95% CI 0.64-1), STSAVRScore: 0.93 (95% CI % 0.86-1), NNEAVRScore: 0.65 (95% CI 0.33-0.97), OntarioScore: 0.93 (95% CI 0.84-1). Regarding calibration, we found that the P value for the Hosmer-Lemeshowed test was: EuroScore logistic: 0.85, EuroScore 2: 0.73, STSAVRScore: 0.93, NNEAVRscore: 0.37 and OntarioScore: 0. 62. Conclusions: The STSAVRScore presented the best discrimination for hospital mortality, although the Euroscores and the Ontario Score behaved well. The STSAVRScore was the scale that achieved the best calibration at the different levels of risk.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".