Management strategies of unanticipated intracranial stenosis during mechanical thrombectomy for acute stroke: A survey of academic neurointerventionalists
Bibliographic record
Abstract
BACKGROUND: The optimal approach to the management of intracranial atherosclerotic disease (ICAD) at the time of mechanical thrombectomy (MT) for large vessel occlusion (LVO) remains controversial. The goal of this study is to characterize current practices concerning this challenging clinical situation in a survey of practicing neurointerventionalists. METHODS: An electronic questionnaire was sent to a cross-section of North American academic neurointerventionalists using publicly available contact information and departmental websites. Prior to analysis, responses were anonymized and categorized by region. RESULTS: A total of 136/360 responses were recorded from the U.S. and Canada. The mean number of years of practicing as a neurointerventionalist among the respondents was 10.5 (± 6.2 years). ICAD was perceived as a causative factor during MT for LVO in 5-10% of thrombectomy cases. The most common first-line treatment approach for significant ICAD, assuming a TICI 2b or better reperfusion, was medical therapy (77.9% of respondents), followed by angioplasty + stent placement (8.8% of respondents). There were no significant differences in the first line treatment of ICAD in LVO between geographical regions (p = 0.815). CONCLUSION: The approach to underlying ICAD in LVO varies widely; however, the majority of neurointerventionalists prefer medical therapy with DAPT as a first-line treatment approach. The current survey highlights the need for studies that better define the optimal timing and modality of treatment, along with an evidence-based framework for balancing the risks associated with these treatment approaches.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".