Analysis of current mortality risk scores for acute type A aortic dissection: The Siena experience
Bibliographic record
Abstract
OBJECTIVE: In literature, various risk scores have been described to predict in-hospital mortality of patients undergoing surgery for acute type A dissection. We want to evaluate which factors are most correlated with a negative outcome and testing the validity of the current scores in literature analyzing our experience of over 20 years in the surgery of type A aortic dissections. MATERIALS AND METHODS: A total of 324 patients were included in the study. Patients were divided into two groups according to 30-day survival or mortality. The preoperative variables analyzed are the parameters necessary for the calculation of scores: Penn Classification, Leipzig Halifax and adjusted Leipzig Halifax score, GERAADA score and EuroSCORE II. Intra- and post-operative mortality were 10.2% and 17.5%, respectively. In multivariate analysis, the preoperative predictors of 30-day mortality were age greater than 70 years, low eject fraction levels, visceral and coronary malperfusion. Both GERAADA and EuroSCORE II were statistically significant predictors of 30-day mortality. However, EuroSCORE II underestimates the mortality compared to GERAADA score probably due to the lack of evaluation of fundamental preoperative factors in the course of type A aortic dissection. RESULTS: The study has demonstrated the efficacy of the GERAADA score in predicting the outcome of patients undergoing surgery and the underestimation of the mortality of EuroSCORE II in our population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".