Propofol Reduces Cognitive Impairment After Electroconvulsive Therapy
Bibliographic record
Abstract
BACKGROUND: Cognitive impairments are the main complication after electroconvulsive therapy (ECT). Modification of treatment parameters has been shown to affect the magnitude of these impairments, but the role of anesthetic type remains unclear. This study tested whether there is a difference in cognitive impairments immediately after ECT with propofol compared to thiopental anesthesia. METHODS: This randomized, double-blind, crossover study included 15 patients receiving right unilateral ECT for depression. Patients received propofol or thiopental on alternating ECTs up to 6 treatments. Immediate and delayed verbal memory, motor speed, reaction speed, visuospatial, and executive functions were assessed 45 minutes after each ECT. Differences were assessed with repeated measures analysis of variance. RESULTS: Cognitive impairments were reduced after ECT with propofol compared to thiopental. Time to emergence was quicker and EEG seizure duration was shorter after propofol treatments. There was no significant correlation between seizure duration and neuropsychological test performance. CONCLUSIONS: Our results indicate that cognitive impairments in the early recovery period after ECT are reduced with propofol compared to thiopental anesthesia. We suggest that, in addition to ECT parameters, the type of anesthetic agent should be considered to reduce cognitive impairments after ECT.
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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) | 0.001 | 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".