Neuroanatomy and severity of stroke in patients with type A aortic dissection
Bibliographic record
Abstract
BACKGROUND: Strokes are a longstanding complication of acute type A aortic dissection (ATAAD) repair. Understanding the neuroanatomy, mechanism, and severity of stroke will facilitate efforts to improve prediction, prevention, and treatment strategies. METHODS: Retrospective review of patients who sustained stroke from a consecutive series of patients undergoing ATAAD repair. Neuroimaging was interpreted by two stroke neurologists blinded to clinical results. Severity of stroke was assessed by the National Institutes of Health Stroke Scale (NIHSS). Residual disability at 30 days was assessed using the modified Rankin Scale (mRS). RESULTS: Twenty percent (38/189) of patients undergoing repair for ATAAD had stroke (unilateral 58%, bi-hemispheric 42% [p = .33]). All strokes were ischemic. No significant lateralization (right vs. left) was noted with unilateral strokes (26% vs. 32%, p = .67). Etiology of stroke was embolic (58%), hypoperfusion (26%), mixed (11%), and unknown (5%). There were no intraoperative variables that correlated with the neuroanatomy or mechanism of stroke. Preoperative carotid dissection was seen in 40% (n = 15), while postoperatively 10% (n = 4) sustained intracranial large vessel occlusion (LVO). Strokes were moderate or severe (NIHSS ≥ 9) in 97% of cases, with 66% incidence of moderate residual disability (mRS ≥ 3) at 1 month postoperatively. CONCLUSIONS: Strokes associated with ATAAD are severe at presentation resulting in significant disability. One in 10 strokes is due to LVO and potentially amenable to endovascular treatment. Heterogeneity in both location and etiology of stroke makes prevention challenging. Future trials may evaluate the role of early neuroimaging and simultaneous treatment of stroke given advancements in endovascular therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".