Early Mortality in Type A Acute Aortic Dissection
Bibliographic record
Abstract
Importance: Early data revealed a mortality rate of 1% to 2% per hour for type A acute aortic dissection (TAAAD) during the initial 48 hours. Despite advances in diagnostic testing and treatment, this mortality rate continues to be cited because of a lack of contemporary data characterizing early mortality and the effect of timely surgery. Objective: To examine early mortality rates for patients with TAAAD in the contemporary era. Design, Setting, and Participants: This cohort study examined data for patients with TAAAD in the International Registry of Acute Aortic Dissection between 1996 and 2018. Patients were grouped according to the mode of their intended treatment, surgical or medical. Exposure: Surgical treatment. Main Outcomes and Measures: Mortality was assessed in the initial 48 hours after hospital arrival using Kaplan-Meier curves. In-hospital complications were also evaluated. Results: A total of 5611 patients with TAAAD were identified based on intended treatment: 5131 (91.4%) in the surgical group (3442 [67.1%] male; mean [SD] age, 60.4 [14.1] years) and 480 (8.6%) in the medical group (480 [52.5%] male; mean [SD] age, 70.9 [14.7] years). Reasons for medical management included advanced age (n = 141), comorbidities (n = 281), and patient preference (n = 81). Over the first 48 hours, the mortality for all patients in the study was 5.8%. Among patients who were medically managed, mortality was 0.5% per hour (23.7% at 48 hours). For those whose intended treatment was surgical, 48-hour mortality was 4.4%. In the surgical group, 51 patients (1%) died before the operation. Conclusions and Relevance: In this study, the overall mortality rate for TAAAD was 5.8% at 48 hours. For patients in the medical group, TAAAD had a mortality rate of 0.5% per hour (23.7% at 48 hours). However, among those in the surgical group, 48-hour mortality decreased to 4.4%.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".