The Impact of Anticholinergic Burden on the Development of Mild Behavioral Impairment
Bibliographic record
Abstract
OBJECTIVE: Mild behavioral impairment (MBI) is a syndrome of late-life-onset persistent neuropsychiatric symptoms. Anticholinergic medication is commonly prescribed in older adults. Both MBI and anticholinergic exposure are associated with increased dementia risk. We sought to understand the association of anticholinergic burden (ACB) with MBI. DESIGN, SETTING, PARTICIPANTS: We mapped ratings on the Neuropsychiatric Inventory Questionnaire to the MBI checklist (MBI-C) using an established algorithm to define MBI status in cognitively unimpaired individuals in the National Alzheimer's Coordinating Center database. We then assessed the association between time-varying ACB ratings and risk of incident MBI. RESULTS: 4865 participants met inclusion criteria and were followed for a mean (SD) of 5.64 (3.92) years. ACB scores ranged from 0 to 11. 63.3% of participants had a score of 0, 27.7% had a score of 1-2, and 9% had a score of ≥3. Higher maximum total ACB score was associated with a higher likelihood of developing MBI (p ≤0.001). When assessed as a time varying covariate, ACB score was associated with incident MBI (HR 1.07, 95% CI 1.02-1.14, p = 0.010). This association remained significant when adjusted for 10-year mortality risk, age, sex, education, and race. CONCLUSIONS: MBI risk should be considered when prescribing anticholinergic medication in older adults.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".