Postoperative cognitive dysfunction after endovascular treatments for unruptured intracranial aneurysms: A pilot study
Bibliographic record
Abstract
Objective Post operative cognitive dysfunction (POCD) has been widely observed after major surgery, particularly in elderly patients with general anesthesia (GA). However, a specific unanswered question is whether different approaches to anesthetic managements are associated with different cognitive outcomes after endovascular treatments for unruptured intracranial aneurysms (UIAs). The purpose of this study is to assess the correlation of POCD with GA versus monitored anesthesia care (MAC). Methods We performed a pragmatic, prospective study to assess the association between different anesthetic approaches and POCD. We compared the pre- and post-procedural Montreal Cognitive Assessment (MoCA) scores in patients with normal cognition who underwent treatments of UIAs with various endovascular methods, using either GA or MAC. Results A total of 23 patients with UIAs were enrolled in the study. Seven (30.4%) and sixteen (69.6%) UIAs were treated without perioperative complications under GA or MAC, respectively. There was a significant decline in the post-procedural MoCA score under GA (mean difference = 1.14; 95% confidence interval = [0.42–1.87], P < 0.01). By contrast, there was no significant difference of MoCA score between pre- and post-procedure under MAC (mean difference = 0.19; 95% confidence interval = [−0.29–0.67], P = 0.59). Conclusions Treating UIAs using MAC was associated with a decrease in POCD as compared to GA in patients undergoing endovascular treatments for UIAs with normal cognition. Larger randomized studies are needed to confirm these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".