The Efficacy and Cognitive Effects of Acute Course Electroconvulsive Therapy Are Equal in Adolescents, Transitional Age Youth, and Young Adults
Bibliographic record
Abstract
Objective: Electroconvulsive therapy (ECT) is the most effective acute treatment for depression, but its use in younger patients is rare and heavily regulated in many U.S. states. It is unclear whether age modifies treatment response or tolerability in adolescents, transitional age youth, and young adults. We examined the effects of ECT on depression and cognition in patients aged 16–30 years. Methods: A retrospective cohort study of patients aged 16–30 years receiving ECT between 2011 and 2020 who were evaluated with the Quick Inventory of Depressive Symptomatology (QIDS), the Behavior and Symptom Identification Scale-24 (BASIS-24), and the Montreal Cognitive Assessment (MoCA) at baseline and following treatment #10. Results: Among the 424 patients who met the inclusion criteria, ECT was associated with a decrease in depression symptoms (ΔQIDS −6.7; Kruskal–Wallis rank sum test; χ 2 = 293.37; df = 2; p < 0.0001) and improvement in overall self-reported mental health status (ΔBASIS-24 − 0.70; Kruskal–Wallis rank sum test; χ 2 = 258.5; df = 2; p < 0.0001) during the first 10 treatments, with a slight reduction in cognition as measured by the MoCA (ΔMoCA −1.1; Kruskal–Wallis rank sum test; χ 2 = 33.7; df = 1; p < 0.0001). Age was not a significant predictor of QIDS, BASIS-24, or MoCA changes. Conclusions: Among 424 patients aged 16–30 years receiving acute course ECT, age was not a significant predictor of improvement in depression, change in overall self-reported mental health status, or change in cognition. These results support the utility of ECT in the treatment of adolescents and young adults.
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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 | 0.001 |
| 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".