Abstract WP28: Factors Associated With Imaging and Endovascular Therapy Decisions for Mild Ischemic Stroke: An International Survey
Bibliographic record
Abstract
Background: Approximately 1 in 7 patients with mild ischemic stroke (NIHSS ≤5) have large vessel occlusion (LVO). These patients were excluded from randomized trials of endovascular therapies (EVT). We conducted a survey to evaluate global practice patterns of EVT in mild stroke with LVO. Methods: International vascular stroke clinicians and interventionalists were invited to participate through professional stroke listservs. The survey consisted of 6 clinical vignettes of mild stroke patients with LVO (Table). Cases varied by NIHSS, neurological symptoms and LVO site. All had same risk factors, time from symptom onset (5h) and unremarkable head CT (high ASPECTS). Advanced imaging data was available upon request. We explored independent case and responder specific factors associated with advanced imaging request and EVT decision. Results: Of 492 responders, 482 had analyzable data ([median age 44 (IQR 11.25)], 78% male, 77% attending, 22% interventionalist). Participants were from USA (45%), Europe (32%), Australia (12%), Canada (6%), and Latin America (5%). EVT was offered to 48% (84% M1, 29% M2 and 19% A2). Treatment decision made without advanced imaging in 66%. In multivariable analysis, proximal occlusion (M1 vs M2 or A2, p<0.001), higher NIHSS (p<0.001) and fellow level training (vs attending; p=0.001) were positive predictors of EVT. Distal occlusion (M2 and A2) and higher age of responder were independently associated with increased advanced imaging requests. Compared to US and Australian responders, Canadians were less likely to offer EVT, while those in Europe and Latin America were more likely (p<0.05). Conclusions: Treatment patterns of EVT in mild stroke vary widely. More proximal occlusion, higher NIHSS, younger physician, fellow level training, and area of residence (Europe and Latin America) were key factors associated with offering EVT. These data suggest that wide equipoise exists in the current approach to this important subset of LVO 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".