Electroconvulsive Therapy for Unipolar Depression in Older Adults
Bibliographic record
Abstract
OBJECTIVES: Electroconvulsive therapy (ECT) is a safe and effective procedure in unipolar depression in older adults; however, less is known about clinical features and trajectories among patients who do not respond. In this retrospective, naturalistic study, we examine characteristics associated with ECT response among older adults with unipolar depression who received ECT over an 8-year period and describe long-term outcomes for nonresponders. METHODS: We retrospectively identified patients 65 years or older with major depressive disorder who were treated with ECT during an 8-year period. We reviewed demographic and clinical factors among patients who responded to ECT and those who did not. Clinic notes were reviewed for ECT nonresponders to determine Clinical Global Impressions scores in the 24 months after ECT treatment. RESULTS: We identified 140 patients meeting the inclusion criteria. Most patients (65%) responded to ECT. Fewer previous antidepressant trials, lower baseline Montreal Cognitive Assessment scores, and lower baseline Montgomery-Asberg Depression Rating Scale scores were associated with an increased likelihood of ECT response. Among the 49 (35%) nonresponders, another 12 (24.5%) responded to a variety of treatments within 2 years after ECT. There were no serious adverse effects of treatment. CONCLUSIONS: Most patients responded to ECT, many of whom had severe illness that had been refractory to numerous medication trials. Among nonresponders, a subset improved over time through a variety of treatments. However, most patients who did not respond to ECT had persistent depression after 2 years.
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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 | 0.000 | 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".