Clinical effectiveness and speed of response of electroconvulsive therapy in treatment‐resistant schizophrenia
Bibliographic record
Abstract
AIM: Although electroconvulsive therapy (ECT) has been shown to be efficacious for patients with treatment-resistant schizophrenia, there has been limited evidence on the rate of response, cognition, and quality-of-life outcomes. The primary aims of the present study were thus to examine the effectiveness and speed of response to ECT in a naturalistic retrospective cohort in patients with treatment-resistant schizophrenia. METHODS: We performed a retrospective database analysis. The primary effectiveness outcome was defined as an improvement of ≥40% from pretreatment scores based on the Brief Psychiatric Rating Scale (BPRS) Psychotic Symptom subscale. Data were included for analysis for all patients with a primary DSM-5 diagnosis of schizophrenia that was treatment-resistant and who had had an acute course of ECT initiated for the treatment of schizophrenia between 1 July 2016 and 1 December 2016. RESULTS: A total of 50 inpatients were included for analysis. The present study revealed that 50% of patients showed at least a 40% reduction in BPRS Psychotic Symptom subscale scores after completion of ECT and that 16.7% of patients responded after the first three sessions, 39.3% after six sessions, 46.4% after nine sessions, and 50% after 12 sessions. The greatest improvement in BPRS scores was between the third and sixth ECT sessions. BPRS scores, Clinical Global Impression, Montreal Cognitive Assessment, and Global Assessment of Functioning showed significant improvement. There was no significant difference in quality-of-life outcomes. CONCLUSION: Utilizing modern techniques in treatment-resistant schizophrenia, this study demonstrates the real-world effectiveness and rate of response of patients receiving ECT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".