P.183 Anesthetic strategies for mechanical thrombectomy: a single-center retrospective review
Bibliographic record
Abstract
Background: Ischemic stroke is a major cause of morbidity and mortality in Canada. Since 2015, mechanical thrombectomy has been the standard of care for eligible large vessel occlusions (LVOs), though anesthetic strategies remain variable. Methods: We conducted a single-center retrospective review of patients undergoing mechanical thrombectomy for anterior circulation LVOs between 2021 and 2023. Patients were categorized by anesthetic strategy (general anesthesia vs. conscious sedation), and outcomes, including time to recanalization, angiographic results (mTICI), and 90-day functional status (mRS), were compared. Statistical analyses included Student’s t-test, Mann-Whitney U-test, and Fisher’s exact test. Results: Among 226 patients, 177 (78%) received general anesthesia and 49 (22%) underwent conscious sedation. Baseline characteristics including sex, age, NIHSS, ASPECTS, collaterals, and laterality were similar between groups. Conscious sedation was associated with a statistically significant shorter time from arrival to the angiography suite to groin puncture (p=0.007), but no differences in time to recanalization (p=0.893), angiographic outcomes (p=0.987), or 90-day functional status (p=0.795) were observed. Conclusions: Conscious sedation led to faster procedural initiation, though no difference in clinical or radiographic outcome was observed. Anesthetic choice should be individualized based on patient and physician factors in acute mechanical thrombectomy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".