Assessing Practice Variation of Anesthetic Management for Endovascular Thrombectomy in Acute Ischemic Stroke: A Comprehensive Multicenter Survey
Bibliographic record
Abstract
OBJECTIVE: This study explored the current global landscape of periprocedural care of acute ischemic stroke patients undergoing endovascular thrombectomy (EVT). METHODS: An anonymous, 54-question electronic survey was sent to 354 recipients in hospitals worldwide. The responses were stratified by World Bank country income level into high-income (HICs) and low/middle-income (LMICs) countries. RESULTS: A total of 354 survey invitations were issued. Two hundred twenty-three respondents started the survey, and 87 fully completed surveys were obtained from centers in which anesthesiologists were routinely involved in EVT care (38 in HICs; 49 in LMICs). Respondents from 35 (92.1%) HICs and 14 (28.6%) LMICs reported that their centers performed >50 EVTs annually. Respondents from both HICs and LMICs reported low rates of anesthesiologist involvement in pre-EVT care, though a communication system was in place in 100% of HIC centers and 85.7% of LMIC centers to inform anesthesiologists about potential EVTs. Respondents from 71.1% of HIC centers and 51% of LMIC centers reported following a published guideline during EVT management, though the use of cognitive aids was low in both (28.9% and 24.5% in HICs and LMICs, respectively). Variability in multiple areas of practice, including choice of anesthetic techniques, monitoring and management of physiological variables during EVT, and monitoring during intrahospital transport, were reported. Quality metrics were rarely tracked or reported to the anesthesiology teams. CONCLUSIONS: This study demonstrated variability in anesthesiology involvement and in clinical care during and after EVT. Centers may consider routinely involving anesthesiologists in pre-EVT care, using evidence-based recommendations for EVT management, and tracking adherence to published guidelines and other quality metrics.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".