Assessment of cognitive function following a course of electroconvulsive therapy
Bibliographic record
Abstract
Introduction ECT is a potentially life-saving treatment for patients with severe or treatment resistant depression. Cognitive function disturbances following ECT are generally transient, but could be of longer duration in some cases Objectives To assess the cognitive side effects in patients with affective disorders treated with a course of electroconvulsive therapy (ECT). Methods Cognitive functions of patients who undergo ECT was assessed prior to start of treatment, midway of the course of treatment and after end of the course of treatment using Montreal Cognitive Assessment (MoCA). We did a retrospective analysis of MoCA scores of 15 patients who received bilateral ECT in 2017-2018. In order to assess the efficacy of ECT in the treatment of their illness, we did a retrospective analysis of Montgomery Asberg Depression Rating Scale (MADRS) scores of 18 patients who received bilateral ECT in 2017-2018 Results Only 7% of the patients who underwent ECT in our sample did have significant cognitive decline as per their MoCA scores. 28% of patients achieved complete remission in their depressive symptomes. 22% of patients continued on maintenance treatment. 95% of patients showed significant improvement in their symptoms following treatment with ECT where there symptoms reduced to either mild or minimal depressive symptoms. Conclusions Cognitive side effect was not a significant side effect in our sample of patients. We did see an improvement in cognitive function in a significant number of the sample of patients as they progressed with treatment, which coincided with improvement in their affective symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".