Effective Use of Electroconvulsive Therapy in Late-Life Depression
Bibliographic record
Abstract
OBJECTIVE: To review literature pertaining to the efficacy, safety, and tolerability of electroconvulsive therapy (ECT) in treating late-life depression. METHOD: We undertook a literature review with an emphasis on research studies published in the last 10 years. RESULTS: There is a positive association between advancing age and ECT efficacy. Age per se does not necessarily increase the risk of cognitive side effects from ECT, but this risk is increased by age-associated neurological conditions such as Alzheimer's dementia and cerebrovascular disease. With appropriate evaluation and monitoring, ECT can be used safely in patients of very advanced age and in those with serious medical conditions. Several technical factors, including dose of electricity relative to a patient's seizure threshold, position of electrodes, frequency of administration, and total number of treatments, have an impact on the efficacy and cognitive side effects of ECT and need to be taken into account when administering ECT. Naturalistic studies have found that 50% of more of patients have a relapse of depression within 6 to 12 months of discontinuing acute ECT. CONCLUSIONS: In recent years, there has been substantial progress in our understanding of the effect of technical factors on the efficacy and cognitive side effects of ECT. When administered in an optimal manner, ECT is a safe, well-tolerated, and effective treatment in older patients. Relapse of depression after response to ECT remains a significant problem, and there is a need for further research into the prediction and prevention of post-ECT relapse.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".