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Record W4391574622 · doi:10.1177/02184923241230344

Analysis of current mortality risk scores for acute type A aortic dissection: The Siena experience

2024· article· en· W4391574622 on OpenAlexaboutno aff
Veronica Lorenz, Luigi Muzzi, Eugénio Neri

Bibliographic record

VenueAsian Cardiovascular and Thoracic Annals · 2024
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEuroSCOREAortic dissectionMultivariate analysisInternal medicineSurgeryCardiologyCardiac surgeryAorta

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.075
GPT teacher head0.413
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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