Abstract 283: Diabetes and Dissection: An Analysis from the International Registry of Acute Aortic Dissection
Bibliographic record
Abstract
Background: Studies have shown that diabetes is less prevalent in acute aortic dissection (AAD) or aortic aneurysm patients (pts) than it is in those with coronary artery disease or heart failure. While diabetes has been found to inhibit aortic aneurysm development in laboratory animals, little is known about its impact on AAD. Methods: Of 3,662 pts enrolled in the International Registry of Acute Aortic Dissection, 248 (6.8%) were diabetic. Diabetic and non-diabetic pts with type A (TA) (n=2371, 6.2% diabetic) and type B (TB) (n=1291, 7.9% diabetic) AAD were compared in this study. Results: Diabetic pts were on average older than non-diabetic for both TA (67.7 vs 61.4 years, p<0.001) and TB (67.8 vs 63.2 years, p<0.001). Pts with TA AAD and diabetes were more often managed medically (18.5%, 27/287 vs 11.7%, 260/2225, p=0.015), while TB diabetic pts were more likely to undergo endovascular procedures (29.4%, 30/102 vs 20.8%, 247/1189, p=0.045). TA diabetics had more in-hospital myocardial infarction (12.9%, 18/139 vs 6.8% 143/2099, p=0.007). Both TA and TB diabetics had more acute renal failure in hospital (TA: 34.3%, 48/140 vs 24.4% 513/2105, p=0.009; TB: 27.7% 28/101 vs 16.8%, 188/1118, p =0.006). In-hospital mortality was similar between groups. TB diabetics had significantly higher follow-up mortality on Kaplan-Meier analysis (p=0.028). Conclusion: Diabetes does not appear to impact treatment selection or in-hospital mortality for pts with AAD. However, it is important to note that diabetic pts demonstrate lower rates of follow-up survival, a trend that reaches significance in type B pts. The lower prevalence of diabetes among IRAD pts compared to other cardiovascular diseases suggests that diabetes may impact the development of AAD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".