Effects of Anticholinergic Burden on Verbal Memory Performance in First-Episode Psychosis
Bibliographic record
Abstract
Antipsychotics are widely used to treat first-episode psychosis but may have an anticholinergic burden, i.e., a cumulative effect of medications that block the cholinergic system. Studies suggest that a high anticholinergic burden negatively affects memory in psychosis, where cognitive deficits, particularly those in verbal memory, are a core feature of the disease. The present study sought to replicate this in a large cohort of well-characterized first-episode psychosis patients. We expected that patients in the highest anticholinergic burden group would exhibit the poorest verbal memory compared to those with low anticholinergic burden and to healthy controls at baseline (3 months following admission). We further hypothesized that over time, at month 12, patients' verbal memory performance would improve but would remain inferior to controls. Patients (n=311; low anticholinergic burden [n=241] and high anticholinergic burden [n=70], defined by a Drug Burden Index cut-off of 1) and controls (n=128) completed a clinical and neurocognitive battery including parts of the Wechsler Memory Scale at month 3 and 12. Cross-sectionally, using an ANOVA, patients in the highest anticholinergic burden group had the poorest performance in verbal memory when compared to the other groups at month 3 (F(2,430)=52.33, P<0.001). Longitudinally, using a Generalized Estimating Equation model, the verbal memory performance of all groups improved over time. However, patients' performance overall remained poorer than the controls. These findings highlight the importance of considering the anticholinergic burden when prescribing medications in the early stages of the disease.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".