Anesthetic Management and Outcome in Patients during Endovascular Therapy for Acute Stroke
Bibliographic record
Abstract
BACKGROUND: Studies of endovascular treatment for acute ischemic stroke have identified general anesthesia as a predictor for poor outcome in comparison with local anesthesia/sedation. This retrospective study attempts to identify modifiable factors associated with poor outcome, while adjusting for baseline stroke severity, in patients receiving general anesthesia. METHODS: We reviewed charts of 129 patients treated between January 2003 and September 2009. The primary outcome was the modified Rankin Score of 0-2 for 3 months poststroke. Predictors of neurologic outcome included baseline National Institutes of Health Stroke Scale score, blood glucose concentration, and age. Additional risk factors evaluated were prolonged stroke onset-treatment interval and systolic blood pressure less than 140 mmHg. Choice of local anesthesia or general anesthesia was recorded. RESULTS: The study group was 96 out of 129 patients for whom modified Rankin Scale scores were available; 48 patients received general anesthesia and 48 local anesthesia. The proportion of patients with "good" outcomes were 15% and 60% in the general anesthesia group and local anesthesia group, respectively (P < 0.001). Lowest systolic blood pressure and general anesthesia were correlated (r = -0.7, P < 0.001). Independent predictors for good neurologic outcome were local anesthesia, systolic blood pressure greater than 140 mmHg, and low baseline stroke scores. CONCLUSIONS: Adjusted for stroke severity, patients who received general anesthesia for treatment are less likely to have a good outcome than those managed with local anesthesia. This may be due to preintervention risk not included in the stroke severity measures. Hypotension, more frequent in the general anesthesia patients, may also contribute.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".