Retrospective analysis of electroconvulsive therapy in treatment-resistant schizophrenia
Bibliographic record
Abstract
University of Medical Sciences between 2000 and 2022.The group included 30 women and 9 men aged 19-74 years (mean 39±11) who received at least 7 ECT sessions.The efficacy of electroconvulsive therapy was assessed using the Clinical Global Impression (CGI) scale -clinical status before treatment and improvement after treatment.Results.In the whole group, the median value in the CGI score before treatment was 6 points, which corresponds to a heavy intensification of clinical symptoms.This value was observed in 34 of 39 patients (87%).The median value in CGI improvement score -that is, the effectiveness of treatment -in all the patients treated for schizophrenia with ECT was 2 points, which may indicate the high effectiveness of the method.The most observed value in the CGI improvement scale was 1 point.This value was observed in 19 of 39 patients (46%).The effectiveness of ECT is negatively correlated with a larger number of hospitalisations.The effectiveness of ECT was not correlated with the type of antipsychotic treatment.Conclusions.These results confirm data from the literature indicating that ECT therapy is effective for treatment-resistant schizophrenia. StreSzczenieCel pracy.Celem pracy była analiza skuteczności leczenia elektrowstrząsami (EW) schizofrenii lekoopornej na podstawie doświadczeń Kliniki Psychiatrii Dorosłych Uniwersytetu Medycznego w Poznaniu. AbStrActObjectives.This study aims to analyse the effectiveness of electroconvulsive therapy (ECT) for treatment-resistant schizophrenia, based on the experience of the Department of Adult Psychiatry, Poznan University of Medical Sciences. Material and methods.The study included 39 patients with treatment-resistant schizophrenia who were treated with ECT at the Department of Adult Psychiatry, Poznan
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".