P.159 Saskatchewan experience with mechanical thrombectomy under general anesthesia
Bibliographic record
Abstract
Background: While mechanical thrombectomy (MT) has become broadly used, many nuances around its performance are still contentious. In particular, the optimal sedation strategy for MT is not clear in the literature. Methods: This study was a single-center retrospective cohort study of a prospectively collected database. Age, gender, pre-treatment NIH stroke score (NIHSS), Alberta stroke program early score CT (ASPECTS), quality of collateralization, whether the patient underwent thrombectomy, tandem carotid occlusion, and thrombolysis in cerebral infarction (TICI) score were recorded in the database. Results: We identified 228 patients having anterior circulation mechanical thrombectomy (MT). 91 were right-sided, 108 were left-sided. Collaterals were graded as good in 135 (71.4), moderate in 44 (23.2%), and poor in 10 (5.3%). The average pre-MT ASPECTS was 8.1 (range). We found significant differences between all patients, patients with good outcome (mRS 0-2) and death in age, baseline NIHSS, collateralization, and TICI revascularization score. Multivariate analysis was performed with showed significant associations of sidedness, collateralization, TICI score and hemorrhage with neurological outcome. Right-sided stroke, better collaterals, higher TICI score and absence of hemorrhage were associated with better outcomes. Conclusions: We found comparable outcomes to those reported in the literature with use of general anesthetic. We identify several factors that influence outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".