The role of surgeon’s intuition for acute type A aortic dissection in an era of evidence-based medicine: a prospective cohort study
Bibliographic record
Abstract
Background: Intuition may play a role in clinical practice. This prospective cohort study aimed to explore whether surgeons' intuition is valid in predicting the operative mortality of acute type A aortic dissection (ATAAD). Methods: After admission (before surgery), attending surgeons were asked to rate the mortality on a scale of 1 to 10, with 1 to 3 representing unlikely, 4-6 possible, and 7-10 very likely. The area under the curve (AUC) of receiver operating characteristic (ROC) analysis was performed to assess the accuracy of prediction models. Results: 8.0 (7.0, 10.0)] was observed in the mortality group, compared to the survival group. The odds ratio (OR) for Surgeon's Score was 1.32 [95% confidence interval (CI): 1.09-1.66, P=0.009]. Least absolute shrinkage and selection operator (LASSO) regression picked the following variables as significant predictors for early mortality of ATAAD: Surgeon's Score, Penn classification, age, aortic regurgitation, coronary artery disease, chronic obstructive pulmonary disease, platelet count, and ejection fraction. The AUC for the German Registry for Acute Aortic Dissection Type A (GERAADA) score and Surgeon's Score were 0.740 (95% CI: 0.625-0.854), and 0.710 (95% CI: 0.586-0.833), respectively. The combined model of GERAADA score and Surgeon's Score yielded an AUC of up to 0.761 (95% CI: 0.638-0.884). Conclusions: Intuition certainly has a place alongside evidence-based medicine. The duet of intuition and statistics-based scoring systems allows us to make more accurate predictions, potentially resulting in more rational clinical decisions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".