Magnetic seizure therapy in an adolescent with refractory bipolar depression: a case report
Bibliographic record
Abstract
Magnetic seizure therapy (MST) has shown efficacy in adult patients with treatment-resistant depression with limited impairment in memory. To date, the use of MST in adolescent depression has not been reported. Here we describe the first successful use of MST in the treatment of an adolescent patient with refractory bipolar depression. This patient received MST in an ongoing open-label study for treatment-resistant major depression. Treatments employed a twin-coil MST apparatus, with the center of each coil placed over the frontal cortex (ie, each coil centered over F3 and F4). MST was applied at 100 Hz and 100% machine output at progressively increasing train durations. Depressive symptoms were assessed using the 24-item Hamilton Depression Rating Scale and cognitive function was assessed with a comprehensive neuropsychological battery. This adolescent patient achieved full remission of clinical symptoms after an acute course of 18 MST treatments and had no apparent cognitive decline, other than some autobiographical memory impairment that may or may not be related to the MST treatment. This case report suggests that MST may be a safe and well tolerated intervention for adolescents with treatment-resistant bipolar depression. Pilot studies to further evaluate the effectiveness and safety of MST in adolescents warrant consideration.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".