Impact of medications, mood state, and electrode placement on ECT outcomes in treatment-refractory psychosis
Bibliographic record
Abstract
BACKGROUND: Treatment-refractory psychosis (TRP) is a significant clinical challenge. While clozapine is frequently effective, alternate or augmentation strategies are often necessary. Evidence supports effectiveness of electroconvulsive therapy (ECT), but questions remain about optimal treatment parameters and impacts of concomitant pharmacotherapy. OBJECTIVE: /Hypothesis: To analyze the impact of clozapine, anticonvulsant medication, mood state, and ECT electrode placement on outcomes in TRP. We hypothesized that ECT would lead to greater reduction in positive symptoms, particularly in patients receiving clozapine. METHODS: Retrospective study in a tertiary TRP program. The Positive and Negative Syndrome Scale (PANSS) was used for clinical outcomes, with positive subscore as primary outcome. Clinical and ECT data were analyzed using a linear modelling approach, controlling for relevant covariates. RESULTS: A total of 309 patients were included. ECT plus clozapine associated with greater improvement in positive, general, and total symptoms than ECT alone. ECT associated with greater improvement in negative symptoms in depressed patients. Bifrontal placement was mostly equivalent to bitemporal, with greater reduction of positive symptoms in patients receiving clozapine, and associated with lower electrical dose in patients on anticonvulsants. Clozapine increased seizure duration, while anticonvulsants decreased it. Anticonvulsant use in ECT patients associated with equivalent to slightly improved symptom reduction. CONCLUSIONS: ECT's benefit in TRP may be greatest in patients receiving clozapine. ECT can improve negative symptoms in depressed TRP patients. Bifrontal placement is effective in TRP. Clozapine and anticonvulsants have opposite effects on seizure duration, but anticonvulsants may not adversely affect clinical outcomes of ECT for TRP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".