Relationship between subjective well-being and aripiprazole: an [11C]raclopride PET study
Bibliographic record
Abstract
Abstract The dopamine blockade by antipsychotics trigger subjective dysphoria. Compared with D2 antagonists, aripiprazole, a D2 partial agonist, was expected to produce a different experience. Indeed, a previous study reported no relationship between the D2 receptor occupancy by aripiprazole and subjective dysphoria, while the D2 receptor occupancy by antagonists was associated with negative subjective experiences. This study revisited the relationship in patients treated with aripiprazole by using an inhibitory Emax model, which enables the individual drug-free binding potential and D2 receptor occupancy to be properly estimated. Eight patients with schizophrenia who have been clinically stable on aripiprazole were enrolled. Assessments including Positive and Negative Syndrome Scale (PANSS) and Subjective Well-being under Neuroleptics Scale (Kv-SWN) were administered. [11C]raclopride PET scan were conducted 2, 26, and 74 h after aripiprazole administration. Regression analysis showed a significant negative association between the D2 receptor occupancy by aripiprazole in the striatum and the Kv-SWN (R2 = 0.55, p = 0.036), but the PANSS total score was not associated with the Kv-SWN (R2 = 0.42, p = 0.080). The negative association between D2 receptor occupancy by aripiprazole and subjective well-being implies that clinicians should find the lowest effective doses of aripiprazole for clinically stable patients to improve their subjective experiences and clinical outcomes.
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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".