Use of Electroconvulsive Therapy Treatment in Adolescents in Singapore
Bibliographic record
Abstract
BACKGROUND: Electroconvulsive therapy (ECT) is a highly effective treatment for schizophrenia and mood disorders; however, most evidence is derived from the adult population, with less evidence in adolescents. We sought to determine the use of ECT in adolescents in the Institute of Mental Health (IMH) and evaluate the treatment outcome. METHODS: We conducted a retrospective naturalistic analysis of ECT registry data of patients aged from 10 to 19 years from March 2017 to March 2023. Descriptive analysis was used to analyze the demographics and clinical characteristics. Paired t tests were used to compare the change in clinical outcome scores, including the Brief Psychiatric Rating Scale (BPRS), Montgomery-Asberg Depression Rating Scale (MADRS), Clinical Global Impressions Scale - Severity (CGI-S), and Montreal Cognitive Assessment (MoCA) before and after 2 weeks of ECT treatment. RESULTS: Fifty-five patients were included for analysis. There was a significant improvement in BPRS ( P < 0.001), MADRS ( P = 0.005), and CGI-S ( P < 0.001), and the average CGI-I score was 2.275 (SD, 0.81), which is equivalent to "much improved" after 6 sessions of treatment. Of all patients, 48.5% showed significant clinical improvement. There was no significant change in MoCA scores ( P = 0.218). CONCLUSIONS: Our preliminary findings show that ECT is a safe, rapid, and effective treatment for psychotic and mood disorders in adolescents. Further studies with a larger sample size and specific subgroup analysis are needed to establish the effectiveness of ECT and identify predictors of response in this population.
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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 | 0.001 |
| 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".