Prevalence of delusions in drug‐naïve Alzheimer disease patients: A meta‐analysis
Bibliographic record
Abstract
Introduction Neuropsychiatric symptoms (NPS) are common at all stages of Alzheimer disease (AD). Delusions in AD are associated with negative clinical consequences and may signal rapid disease progression. Hence, we sought to determine the prevalence of delusions in drug‐naïve (no cholinesterase inhibitor or neuroleptic medications) AD patients. Methods In this meta‐analysis, a search of the EMBASE, MEDLINE, and PsycINFO databases was performed. We selected studies reporting delusion prevalence measured by the Neuropsychiatric Inventory (NPI) in drug‐naïve AD patients. An aggregate delusion event rate with 95% confidence interval (CI) was calculated. The I2 statistic was used to assess the magnitude of between‐study heterogeneity. Single variable meta‐regressions allowed examination of the effect of moderating factors and heterogeneity. Quantitative measures were used to appraise for publication bias. Results We identified 6 studies with 591 participants allowing calculation of the aggregate delusional prevalence rate. Irrespective of dementia severity, the aggregate event rate for delusions was 29.1% (95% CI: 20–41%; I2 = 84.59). No publication bias was observed. Conclusion This meta‐analysis calculates a 29.1% prevalence rate of delusions in AD patients. There is a trend towards increasing delusion prevalence in concordance with increasing severity of dementia. Given delusions are associated with poorer outcomes, the obtained prevalence should motivate clinicians to screen carefully for delusions. Current literature limitations warrant future studies, with sub‐analyses on dementia severity, and other neurobiological factors known to influence the presence of delusions.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".