Abstract 274: Follow-Up of Young Acute Aortic Dissection Patients: How They Differ from Older Patients
Bibliographic record
Abstract
Background: Young patients (pts) with acute aortic dissection (AAD) have distinct risk factors and presenting symptoms compared to older pts, but whether these differences extend past discharge is relatively unknown. Methods: Among pts presenting with AAD enrolled in the International Registry of Acute Aortic Dissection, pts <40 (N=280) were compared with pts ≥ 40 (N=3585). Chi-square analysis or Fischer’s Exact test were performed for categorical variables; age was compared using Student’s T-test. Kaplan-Meier curves were generated for freedom from adverse events rates 0-60 months following discharge. Mean follow-up was 28.6 months. Results: Significant differences in demographics and history were noted between pts <40 and the older cohort. Young pts more commonly had type A AAD (71.8%, 201/280, v. 64.6%, 2317/ 3585, p<0.016), while type B AAD was more typical in older pts (p<0.016). On imaging studies, pts <40 were less likely to present with IMH (7.3%, 246/3355, v. 2.3%, 6/266, p=0.002), but were more likely to have a patent false lumen (77.9%, 141/181, v. 62.1%, 1425/2295, p<0.001). Surgical management was more common in young pts, for both AAD types. In-hospital complications or mortality did not differ between groups. Kaplan-Meier analysis demonstrated better long-term survival in young pts compared to those ≥ 40 (p=0.029). Kaplan-Meier analyses of freedom from adverse events at 5 years illustrated no difference in aortic growth between groups, but significantly more late interventions in younger pts (p=0.006). Conclusions: Young pts show distinct differences in comparison to older pts, specifically regarding presentation, AAD type and management. Long-term survival and follow-up intervention rates are higher in young pts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".