Abstract WMP69: Imaging Analysis to Assess Stroke Mechanism in Aortic Dissection
Bibliographic record
Abstract
Introduction: Stroke is a frequent complication in patients having surgery for Acute Type A Aortic Dissection (ATAAD) where a variety of neurocirculatory techniques are used to reconstruct the arch. In order for surgeons to prevent stroke, first an understanding of the dynamic hemodynamic processes that cause stroke is required. Objective of this study is to determine radiological characteristics and mechanisms of stroke in ATAAD. So far, surgical literature has only reported on incidence. Methods: From 2006-2016, of 189 ATAAD patients at a single centre, 42 had a stroke. Charts and brain imaging were reviewed by 2 Neurologists. Great vessel pathology along with willisian collateral status was identified. Stroke territory (anterior or posterior circulation), distribution (unilateral or bilateral) and number of lesions were identified. Mechanism of stroke was classified as hypoperfusion (water-shed, borderzone pattern), embolic (cortical multiple or wedge shaped) or combined. Results: All strokes were ischemic. Mean patient age 60.1 +/- 13.1 years. Stroke was diagnosed a median 3.0 days (IQR = 5.1) post operatively. NIHSS classification was mild in 7%, moderate in 81% and 12% had severe. Most strokes were multiple (65%) and in the anterior circulation (88% vs. 32 % posterior). Most patients had an embolic pattern of stroke (45% vs. 36% hypoperfusion vs 17% mixed). Detailed clinical and radiologic data are shown in Table 1. Conclusion: Stroke after ATAAD is ischemic, predominantly embolic, involves both hemispheres and results in significant morbidity. It remains unclear if stroke is a result of the dissection disease process extending to the brain vs. iatrogenically caused by choice and methodology of intraoperative surgical neurocirculatory management strategies used to reconstruct the aorta. Further studies will look for associations between pre-operative cerebrovascular abnormalities, intra-operative management, and mechanisms of stroke.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".