Clozapine response and pre-treatment EEG-is there some kind of relationship
Bibliographic record
Abstract
BACKGROUND: Clozapine has been used widely in the management of treatment-resistant schizophrenia. The present study aims at determining whether pre-treatment electroencephalography (EEG) abnormalities would serve as a marker for response to clozapine treatment. SUBJECTS AND METHODS: This was a cross-sectional study done in a tertiary care center in Mumbai where patients diagnosed with schizophrenia using DSM-IV criteria and resistant schizophrenia using Kane criteria were assessed using EEG prior to starting clozapine treatment. They were rated for symptomatic improvement using the Positive and Negative Syndrome Scale (PANSS) along with Clinical Global Improvement for Severity (CGI-S). The results were statistically analysed and presented. RESULTS: 55 out of the 80 patients in the study showed baseline EEG abnormalities. The mean duration of illness in the patients were 2.65 years. Slow wave and background EEG abnormalities were common in pre-treatment EEG. 36.4% patients in the study showed clinical response. Patients with negative symptoms and baseline EEG abnormalities showed better response. CONCLUSIONS: The study was circumscribed and had many limitations due to a small sample size. The relation between pre-treatment EEG abnormalities and clozapine response could not be statistically correlated and it could not be ascertained to be a marker for response to clozapine therapy.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".