Cognitive functioning in patients treated with electroconvulsive therapy
Bibliographic record
Abstract
BACKGROUND: Electroconvulsive therapy (ECT) is an effective treatment method for severe mental illnesses. ECT has gone through significant modernization. Side effects of ECT have largely decreased. Temporary disturbance of cognitive performance can be still present as a side effect of electroconvulsive treatment. METHODS: Cognitive functioning in the sample of patients with severe and acute mental illness treated with electroconvulsive therapy (ECT) was assessed. Basic assessment of cognitive functions was applied in the beginning, in the middle, and at the end of ECT course treatment with the Montreal Cognitive Assessment (MoCA). Complex and detailed testing of cognitive functions using the MATRICS Consensus Cognitive Battery (MCCB) was done at two points in time - within the first week of and then 6 weeks after the end of ECT. RESULTS: Participants had cognitive deficits at baseline, which were most likely influenced markedly by the psychopathology of the illness itself. The improvement in cognition came together with the reduction in psychopathology; psychopathology scores were significantly reduced during ECT treatment. Compared to the baseline, all scores for cognitive testing were significantly improved but remained low in comparison with the controls. After 6 weeks, there was further significant improvement. CONCLUSION: Our results confirm the safety and efficacy of ECT in the treatment of severe mental disorders.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".