Abstract 20296: In-Hospital Outcomes for Type A Acute Aortic Dissection Patients Presenting With Abnormal Admission Electrocardiogram
Bibliographic record
Abstract
Introduction: Electrocardiogram (ECG) is often used to assist in the diagnosis of patients presenting with chest pain to emergency departments. Given that this type of pain is a common manifestation of Type A Acute Aortic Dissection (TAAAD), ECGs are obtained in much of this population. This study evaluated the impact of particular ECG patterns on the diagnosis and treatment of TAAAD. Methods: TAAAD patients (N=2765) enrolled in the International Registry of Acute Aortic Dissection were stratified into groups based on normal (N=1094, 39.6%) and abnormal (N=1671, 60.4%) presenting ECGs, and further subdivided according to specific ECG findings. Time data is presented in hours as medians (Q1-Q3). Results: Patients with abnormal ECG findings presented to the hospital sooner after symptom onset than those with normal ECGs (1.4 (0.8-3.3) v 2.0 (1.0-3.3); p=0.005). Specifically, shorter time from symptom onset to presentation was seen in patients with infarction with new Qs or ST elevation (1.3 (0.6-2.7) v 1.5 (0.8-3.3); p=0.049). Abnormal ECG findings were associated with less frequent surgical management (84.7% v 92.8%; p Conclusions: An abnormal ECG in patients with TAAAD remains an adverse prognosticator identifying a group of patients who are sicker, have more in-hospital complications, and are more likely to die. That nonspecific ST-T abnormalities are seen in more than 40% of TAAAD patients are associated with a delay in diagnosis and treatment suggests that this is an area for further study.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".