Abstract WP4: Which Characteristics Determine the Decision to Proceed With Endovascular Therapy in Acute Stroke Patients: Results From an International Multidisciplinary Study
Bibliographic record
Abstract
Introduction: The current management of acute ischemic stroke has changed recently with the publication of randomized trials using endovascular therapy (EVT). Rationale/Hypothesis: Limited information is however available on how physicians make decisions regarding patient selection for endovascular therapy (EVT) in the real-world. Methods: We conducted an international web-based cross-sectional survey of stroke physicians and interventionalists to assess the decision to offer EVT. Part 1 of the survey used hierarchical Bayes’ disaggregate discrete choice modelling to analyze ten pairs of patient scenarios, randomly generated from ten key patient characteristics to have respondents choose which scenario was best suited for EVT in their practice. Part 2 of the survey used mixed effects logistic regression modelling to analyze 22 randomly chosen patient scenarios, again randomly generated from several key patient and hospital level characteristics. Results: 607 physicians [mean age of 44 (SD 8.5) years, 83.5% men, 53.6% neurologists, 28.7% neuro-interventionists, 13.3% neurosurgeons, 4.7% other], from 38 countries participated. Using disaggregate discrete choice analysis, the most influential characteristic in deciding about EVT was the extent of ischemic change (ASPECTS)/volume of infarct core (26-28% of the choice). Patient age, premorbid status, baseline NIHSS score, and occlusion location (13-15% each) were the other relevant characteristics. Using mixed effects logistic regression, baseline stroke severity (NIHSS> 15 vs. NIHSS 0-5, OR 6.7; 95% CI 4.8-9.5), ASPECTS (5-7 vs. 0-4, OR 9.4; 95% CI7.4-11.9) and occlusion location (distal M2 vs. ICA/M1, OR 012; 95% CI 0.08-0.17) were the most relevant characteristics in deciding about EVT. Time from stroke onset, sex, comorbidities, time of day (off hours vs. day time) were all less relevant in deciding about EVT in both analyses. Conclusion: Severity of stroke assessed clinically, and extent of brain infarction and location of thrombus assessed on imaging are the dominant characteristics that treating physicians use in the real world when deciding about EVT for patients with an acute ischemic 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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".