Prediction of Working Memory Performance in Schizophrenia by Plasma Ratio of Clozapine to<i>N</i>-Desmethylclozapine
Bibliographic record
Abstract
OBJECTIVE: Clozapine's potent antagonism of muscarinic M1 receptors is thought to worsen working memory deficits associated with schizophrenia. In contrast, its major metabolite, N-desmethylclozapine (NDMC), is thought to enhance working memory via its M1 receptor agonist activity. The authors hypothesized that the ratio of serum clozapine and NDMC concentrations would be inversely associated with working memory performance in schizophrenia. METHOD: Thirty patients with schizophrenia or schizoaffective disorder who were receiving clozapine monotherapy at bedtime completed the MATRICS Consensus Cognitive Battery (MCCB) on the day their blood was collected to assess concentrations of clozapine and NDMC as well as serum anticholinergic activity. RESULTS: The clozapine/NDMC ratio was significantly and negatively associated with working memory performance after controlling for age, gender, education, and symptom severity. No significant associations were found between individual clozapine and NDMC concentrations and working memory performance. Serum anticholinergic activity was significantly associated with clozapine concentration, but not with working memory performance or NDMC concentration. No significant associations were found between any pharmacological measure and performance on other MCCB cognitive domains. CONCLUSIONS: This hypothesis-driven study confirms that clozapine/NDMC ratio is a strong predictor of working memory performance in patients with schizophrenia. This finding suggests that manipulating the clozapine/NDMC ratio could enhance cognition in patients with schizophrenia treated with clozapine. It also supports the study of procholinergic agents, such as M1 receptor-positive allosteric modulators, to enhance cognition in schizophrenia.
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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 | 0.001 | 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".