Use of Electroconvulsive Therapy in Adolescents With Treatment-Resistant Depressive Disorders
Bibliographic record
Abstract
OBJECTIVES: This study presents a comprehensive case series of adolescents who received electroconvulsive therapy (ECT) for treatment-resistant depression. METHODS: Conducting a chart review, we identified 13 adolescents who had ECT for treatment of depression over a 5-year interval (2008-2013) at a Canadian tertiary care psychiatric hospital. Details about participants' clinical profile, index course of ECT, outcome, side effects, and comorbidities were extracted and analyzed. RESULTS: Thirteen adolescents aged 15 to 18 years, received a mean of 14 (SD, 4.5) ECT sessions per patient. Based on the Beck Depression Inventory-II at baseline and after treatment with ECT, a reliable improvement was observed in 10 patients, with 3 achieving full recovery. Through mixed effects linear modeling, we found a decrease of 0.96 points (95% CI, -1.31 to -0.67, P < 0.001) on the Beck Depression Inventory-II total score for every ECT treatment received. The Montreal Cognitive Assessment was used for monitoring of cognitive function throughout the treatment. Adverse effects included transient subjective cognitive impairment (n = 11), headache (n = 10), muscular pain (n = 9), prolonged seizure (n = 3), and nausea and/or vomiting (n = 3). CONCLUSIONS: A clinically significant improvement was observed for 10 (77%) adolescents receiving ECT for treatment-resistant depression. These observations suggest that ECT is a potential treatment option for refractory depression in selected adolescents. More data are needed to draw conclusions about efficacy and possible predictors of treatment response.
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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".