EEG Abnormalities and Outcome in First-Episode Psychosis
Bibliographic record
Abstract
OBJECTIVE: There is widespread consensus that the EEG is not useful for the detection of clinically relevant abnormalities in patients with psychosis. Given that the EEG records brain dysfunction, this study examines whether an abnormal EEG in first-episode psychosis patients is associated with poorer prognosis, compared with a normal EEG. METHOD: At their initial assessment, 61 patients with first-episode psychosis had an EEG classified according to the following modified Mayo Clinic system: normal, essentially normal (that is, one or more elements of questionable normality), or dysrhythmia (grade I to V). We assessed psychiatric symptoms using the Scale for Assessment of Negative Symptoms (SANS) and the Scale for Assessment of Positive Symptoms (SAPS) on entry and after 1 year of treatment. Psychosis is considered to have remitted if there are no, or minimal, psychotic symptoms (that is, a rating of 2 or less on every SAPS global rating), maintained for 1 month. RESULTS: At the end of 1 year, 19/21 (90.5%) patients with a normal EEG had a remission of their positive symptoms, compared with 18/28 (64.3%) of those with an essentially normal EEG and only 7/12 (58.3%) of those with dysrhythmia. Negative symptoms were reduced by more than 50% in 11/18 (61.1%) patients with a normal EEG, compared with 10/28 (35.7%) patients with an essentially normal EEG. None of the 8 patients with dysrhythmia on their EEG experienced reduced negative symptoms. CONCLUSION: The above findings suggest that an abnormal EEG in patients with first-episode psychosis is associated with a poorer prognosis.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".