Pre-Treatment EEG and It's Relationship to Depression Severity and Paroxetine Treatment Outcome
Bibliographic record
Abstract
An array of variables have been assessed as potential early predictors of antidepressant response in depressed patients. This exploratory study examined the relationship of clinical outcome, following pharmacotherapeutic treatment, with quantitative electroencephalographic (EEG) features assessed prior to treatment onset. In 70 major affective disorder patients, pre-treatment spectrum-analysed topographic EEG indices (absolute power, relative power, mean frequency, inter-hemispheric power asymmetry and coherence for 4 frequency bands) were assessed in relation to baseline HAM-D ratings and HAM-D rating changes following 6 weeks of open-label paroxetine treatment. EEG slow wave (theta) activities were positively correlated with depression ratings prior to treatment. Of the patients (n = 51) completing treatment, 80% evidenced a >50% reduction in HAM-D ratings. Improved rating changes in general were found to be negatively related to slow (delta and theta) wave activity and positively related to fast (beta) activity at frontal recording sites. Findings are discussed in relation to the neurochemistry and neurobiology of depressive disorders.
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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 | 0.002 |
| 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 | 0.000 |
| 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".