Abstract 19936: Trends in the Management of Acute Type A Intramural Hematoma
Bibliographic record
Abstract
Introduction: Acute Type A aortic Intramural Hematoma (IMH), a subgroup of aortic dissection, has evoked discussion regarding optimal management of this condition. While some centers have advocated for medical management, recent guidelines have stated that surgical therapy is generally indicated. This study sought to utilize the International Registry of Acute Aortic Dissection to investigate trends over time in the management of this condition. Methods: Of 3503 Type A patients enrolled in the International Registry of Acute Aortic Dissection, 141 (4.0%) were identified as having an IMH. This cohort was stratified by date of presentation into three time periods of equal length: Group 1, 1996-2003 (N=28, 19.9%); Group 2, 2004-2009 (N=30, 21.3%); and Group 3, 2010-2015 (N=83, 58.9%). Results: The study population was 47.5% female with an average age of 70.6 years. Surgery was performed in 71.4% of IMH patients in Group 1, increasing to 81.9% in Group 3 (p=0.492, trend p=0.261), which was not statistically significant. Reasons for receiving medical versus surgical management included advanced age (N=8, 47.1%), comorbid illness (N=12, 57.1%), and patient refusal (N=3, 23.1%). IMH of the arch was identified as a reason for medical management in 6 patients (47.1%). There was no difference in time to diagnosis between groups, with medians of 3.0 hours in Group 1 to 2.6 hours in Group 3 (p=0.971). Notably, surgical mortality decreased between 1996 to 2015, from 35.0% in Group 1 to 13.2% in Group 3 (p=0.087, trend p=0.030). Conclusions: Operative mortality for Type A IMH decreased over time, providing further support for surgical repair in these patients. In the absence of personalized risk models for surgical and medical outcomes utilizing imaging and genomic data, surgical management appears to be the optimal therapy for acute Type A IMH.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".