PET Study of the Effects of Valproate on Dopamine D<sub>2</sub>Receptors in Neuroleptic- and Mood-Stabilizer-Naive Patients With Nonpsychotic Mania
Bibliographic record
Abstract
OBJECTIVE: A previous study reported a higher than normal density of dopamine D(2) receptors in psychotic mania but not in nonpsychotic mania. The purpose of this study was to further examine D(2) receptor density in a larger sample of nonpsychotic manic patients by using positron emission tomography (PET) and [(11)C]raclopride. METHOD: Thirteen neuroleptic- and mood- stabilizer-naive patients with DSM-IV mania without psychotic features and 14 healthy comparison subjects underwent [(11)C]raclopride PET scans. Of the 13 patients, 10 were treated with divalproex sodium monotherapy. PET scans were repeated 2-6 weeks after commencement of divalproex sodium. D(2) receptor binding potential was calculated by using a ratio method with the cerebellum as the reference region. RESULTS: The [(11)C]raclopride D(2) binding potential was not significantly different in manic patients than in the comparison subjects in the striatum. Treatment with divalproex sodium had no significant effect on the [(11)C]raclopride D(2) binding potential in manic patients. There was no correlation between the D(2) binding potential and manic symptoms before or after treatment. CONCLUSIONS: These results suggest that D(2) receptor density is not altered in nonpsychotic mania and that divalproex sodium treatment does not affect D(2) receptor availability.
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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 | 0.001 |
| 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".