Optimizing subjective wellbeing with amisulpride in first episode schizophrenia or related disorders
Bibliographic record
Abstract
Abstract Background Subjective response (SR) to antipsychotic medication is relevant for quality of life, adherence and recovery. Here, we evaluate (1) the extent of variation in SR in patients using a single antipsychotic; (2) the association between subjective and symptomatic response; and (3) predictors of SR. Methods Open-label, single treatment condition with amisulpride in 339 patients with a first episode of a schizophrenia spectrum disorder, at most minimally treated before inclusion. Patients were evaluated at baseline, before start with amisulpride and after four weeks of treatment with the Subjective Wellbeing under Neuroleptic scale, the Positive and Negative Syndrome Scale, and the Calgary Depression Scale for Schizophrenia. Results (1) 26.8% of the patients had a substantial favorable SR, and 12.4% of the patients experienced a substantial dysphoric SR during treatment with amisulpride. (2) Modest positive associations were found between SR and 4 weeks change on symptom subscales (r = 0.268–0.390, p values < 0.001). (3) Baseline affective symptoms contributed to the prediction of subjective remission, demographic characteristics did not. Lower start dosage of amisulpride was associated with a more favorable SR (r = −0.215, p < 0.001). Conclusions We conclude that variation in individual proneness for an unfavorable SR is substantial and only modestly associated with symptomatic response. We need earlier identification of those most at risk for unfavorable SR and research into interventions to improve SR to antipsychotic medication in those at risk.
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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 | 0.001 | 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".