Factors Associated With the Decision-Making on Endovascular Thrombectomy for the Management of Acute Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose- Little is known about the real-life factors that clinicians use in selection of patients that would receive endovascular treatment (EVT) in the real world. We sought to determine patient, practitioner, and health system factors associated with therapeutic decisions around endovascular treatment. Methods- We conducted a multinational cross-sectional web-based study comprising of 607 clinicians and interventionalists from 38 countries who are directly involved in acute stroke care. Participants were randomly allocated to 10 from a pool of 22 acute stroke case scenarios. Each case was classified as either Class I, Class II, or unknown evidence according to the current guidelines. We used logistic regression analysis applying weight of evidence approach. Main outcome measures were multilevel factors associated with EVT, adherence to current EVT guidelines, and practice gaps between current and ideal practice settings. Results- Of the 1330 invited participants, 607 (45.6%) participants completed the study (53.7% neurologists, 28.5% neurointerventional radiologists, 17.8% other clinicians). The weighed evidence approach revealed that National Institutes of Health Stroke Scale (34.9%), level of evidence (30.2%), ASPECTS (Alberta Stroke Program Early CT Score) or ischemic core volume (22.4%), patient's age (21.6%), and clinicians' experience in EVT use (19.3%) are the most important factors for EVT decision. Of 2208 responses that met Class I evidence for EVT, 1917 (86.8%) were in favor of EVT. In case scenarios with no available guidelines, 1070 of 1380 (77.5%) responses favored EVT. Comparison between current and ideal practice settings revealed a small practice gap (941 of 6070 responses, 15.5%). Conclusions- In this large multinational survey, stroke severity, guideline-based level of evidence, baseline brain imaging, patients' age and physicians' experience were the most relevant factors for EVT decision-making. The high agreement between responses and Class I guideline recommendations and high EVT use even when guidelines were not available reflect the real-world acceptance of EVT as standard of care in patients with disabling acute ischemic stroke.
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 | 0.012 | 0.084 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".