Factors influencing thrombectomy decision making for primary medium vessel occlusion stroke
Bibliographic record
Abstract
BACKGROUND: We aimed to explore the preference of stroke physicians to treat patients with primary medium vessel occlusion (MeVO) stroke with immediate endovascular treatment (EVT) in an international cross-sectional survey, as there is no clear guideline recommendation for EVT in these patients. METHODS: In the survey MeVO-Finding Rationales and Objectifying New Targets for IntervEntional Revascularization in Stroke (MeVO-FRONTIERS), participants were shown four cases of primary MeVOs (six scenarios per case) and asked whether they would treat those patients with EVT. Multivariable logistic regression with clustering by respondent was performed to assess factors influencing the decision to treat. Dominance analysis was performed to assess the influence of factors within the scenarios on decision making. RESULTS: Overall, 366 participants (56 women; 15%) from 44 countries provided 8784 answers to 24 scenarios. Most physicians (59.2%) would treat patients immediately with EVT. Younger patient age (incidence rate ratio (IRR) 1.24, 99% CI 1.19 to 1.30), higher National Institutes of Health Stroke Scale (NIHSS) score (IRR 1.69, 99% CI 1.57 to 1.82), and small core volume (IRR 1.35, 99% CI 1.24 to 1.46) were positively associated with the decision to treat with EVT. Interventionalists (IRR 1.26, 99% CI 1.01 to 1.56) were more likely to treat patients with MeVO immediately with EVT. In the dominance analysis, factors influencing the decision in favor of EVT were (in order of importance): baseline NIHSS, core volume, alteplase use, patients' age, and occlusion site. CONCLUSIONS: Most physicians in this survey were interventionalists and would treat patients with MeVO stroke immediately with EVT. This finding supports the need for robust clinical evidence.
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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 | 0.003 | 0.021 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".