Association of 9-Hydroxy Risperidone Concentrations With Risk of Switching or Discontinuation in the Clinical Antipsychotic Trial of Intervention Effectiveness-Alzheimer's Disease Trial
Bibliographic record
Abstract
Risperidone has been used to treat behavioral symptoms, such as delusions and agitation, in people with Alzheimer's disease. The relationship between magnitude and variability of risperidone and 9-hydroxy risperidone exposure and the relationship with time to discontinuation of the medication were explored. Sixty-five subjects from the Clinical Antipsychotic Trial of Intervention Effectiveness-Alzheimer's Disease Trial that received risperidone were included in this study. Eighteen subjects completed the study without switching medication (completers on risperidone), whereas 47 discontinued the medication. Those who discontinued were divided into 2 groups according to responsiveness to therapy. Using Cox proportional survival regression analysis, we estimated time to discontinuation and factors associated with treatment discontinuation including age, dose, body mass index, neuropsychiatric inventory baseline score, and average exposure (area under the curve [AUC]) to risperidone and 9-hydroxy risperidone. Twenty-four and 17 subjects discontinued therapy because of inadequate therapeutic effect and side effects, respectively (6 subjects were excluded because of missing information about reason for switching or discontinuation). Discontinuation hazards for those with a higher than median AUC of the metabolite were 2.54 (P = 0.029; inadequate and side effect group combined) and 3.48 (P = 0.025; inadequate effect group) times that of those in the lower than median AUC group. None of the other covariates contributed significantly to the switching hazard. Risperidone metabolite, 9-hydroxy risperidone concentrations, correlated with the risk of switching or discontinuing the medication, suggesting that 9-hydroxy risperidone contributes to adverse events and intolerability in dementia patients.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".