The impact of anesthesia methods on early postoperative cognitive function in Moyamoya disease patients after vascular bypass surgery
Bibliographic record
Abstract
Moyamoya disease (MMD) patients often experience cognitive dysfunction following vascular bypass surgery, with anesthesia potentially influencing recovery. This study aims to evaluate the effects of IVA and combined intravenous and inhalational anesthesia (CIA) on cognitive recovery in MMD patients and explore influencing factors. We included 120 MMD patients who underwent vascular bypass surgery from January 1, 2021, to January 31, 2023. Patients were divided into 2 groups based on anesthesia method: intravenous anesthesia group (n = 56) and CIA group (n = 64). Cognitive function was assessed using mini-mental state examination and Montreal cognitive assessment preoperatively and at 1 week, 1 month, and 3 months postoperatively. Multivariable regression analysis was used to identify factors affecting cognitive recovery. The CIA group showed better cognitive recovery at 1 week, 1 month, and 3 months postoperatively, but the differences between groups were not statistically significant (P > .05). Multivariable regression analysis showed that anesthesia method was not an independent factor influencing recovery, while preoperative cognitive status, age, and comorbidities were significant predictors. Combined intravenous and inhalational anesthesia may offer some advantage for cognitive recovery in MMD patients after vascular bypass surgery, though the differences were not statistically significant. Preoperative cognitive status, age, and comorbidities are key factors in recovery. Anesthesia protocols should be personalized to optimize postoperative cognitive function.
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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 | 0.002 |
| 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) | 0.000 | 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".