Tolerability and Clinical Outcomes with Anesthesia Dose Reduction during Electroconvulsive Therapy
Bibliographic record
Abstract
BackgroundThere is a high level of inter-individual variability in cognitive performance after an acute treatment course of Electroconvulsive therapy (ECT).As a result, the influence of various individual factors on cognitive outcomes post ECT have been studied.This study aimed to extend prior research by assessing whether a combination of two individual factorsdeducational level and lifetime occupational attainmentdmight be informative.Methods This was a retrospective study with naturalistic data collected from 24 patients with a major depressive episode who underwent acute ECT treatment.Mood and cognitive assessments were conducted at pretreatment, during the ECT course, and 1e3 days post-ECT treatment.Participants were classified into higher or lower functioning groups based on their educational level and lifetime occupational attainment.Results Statistically significant differences were found between groups in retrograde memory following ECT, measured by percentage consistency scores on the Columbia Autobiographical Memory InterviewdShort Form (F(1,15)¼4.66;p<0.05), and recovery of orientation during the acute ECT course (F(1,25.33)¼7.99;p¼0.009).No statistically significant differences were observed between groups in other cognitive domains, such as verbal and visual anterograde memory, verbal fluency, or processing speed.Conclusions These preliminary findings suggest that individuals with higher educational level and occupational attainment may experience less retrograde amnesia for autobiographical information after an acute course of ECT and demonstrate faster recovery of orientation after each ECT sessions.Identifying markers of 'cognitive potential' prior to ECT could help tailor treatment to each individual patient.
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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 | 0.004 |
| 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".