Augmentation of Antipsychotics with Electroconvulsive Therapy in Treatment-Resistant Schizophrenia Patients with Dominant Negative Symptoms: A Pilot Study of Effectiveness
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to determine the effectiveness of the augmentation of antipsychotics (AP) with electroconvulsive therapy (ECT) in treatment-resistant schizophrenia (TRS) patients with dominant negative symptoms. METHODS: The study encompassed 34 patients aged 21-55 years, 47.1% of whom were female, who were diagnosed with TRS. Each patient underwent a course of ECT sessions combined with AP medications which had previously been found to be ineffective. Prior to ECT and within 3 days after the final ECT session, the participants were evaluated on the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia and the Clinical Global Impression scales. RESULTS: Augmentation of AP therapy with ECT led to a significant decrease in symptom severity in TRS patients with dominant negative symptoms, 58.8% of whom demonstrated at least a 25% decrease in the total PANSS score. The greatest reductions were observed in the general and positive PANSS subscales (mean ± SD: 11.35 ± 7.43 and 6.79 ± 5.23 patients), and the least significant in the negative symptoms subscale (5.03 ± 4.36 patients). CONCLUSION: Augmentation of AP therapy with ECT in a group of TRS patients with dominant negative symptoms induced a significant decrease in symptom severity. The greatest reductions were obtained in general and positive symptoms and the least in negative symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".