Risk of Mild Cognitive Impairment and Its Relation to Anticholinergics Revealed Within Screening in a Community Pharmacy
Bibliographic record
Abstract
Abstract Background The increasing prevalence of cognitive impairments (CI), such as Alzheimer's disease and dementia, requires a multidisciplinary approach to prevention. Brief cognitive screening in community pharmacies can help identify CI in early stages, within advanced pharmaceutical care, and effectively reduce the workload of ambulatory care. Study objectives were to assess the association between patients' cognitive ability as determined by standardised cognitive screening and the presence of modifiable Dementia Risk factors, as well as at-risk medication use related to CI within pharmaceutical counselling in community pharmacies. Methods Data collection was realised between 2018 and 2019, retrospective analysis 2024–2025. This study retrospectively analysed data from a clinical cohort study (N = 323). A score expressed cognitive abilities in a short version of the Montreal Cognitive Assessment (s-MoCA), with a cut-off score of ≤ 12 for CI. Dementia risk factors were assessed according to the Cardiovascular Risk Factors, Ageing, and Incidence of Dementia (CAIDE) Risk Score and the cumulative effect of at-risk medications with anticholinergic activity on a person's cognitive function by the Anticholinergic Cognitive Burden Scale (c-ACB). Results Within the provision of pharmaceutical service, 80.50% (N = 260) of respondents were identified with lower cognitive abilities under a normal s-MoCA score. The global mean s-MoCA (± SD) was 9.54 ± 3.38 points. 29.72% (N = 96) of patients had CAIDE 10 + , and 57.59% (N = 186) of patients used at-risk medication (c-ACB 3 +). We found a significant relationship between s-MoCA and CAIDE/c-ACB, respectively ( P < 0.001; R = − 0.2013; and P < 0.0001; R = − 0.3961). Conclusions Cognitive screening in community pharmacies can help identify patients with MCI. In addition, evaluation of CAIDE and at-risk medication use can improve advanced pharmaceutical care for patients with a high risk of CI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".