Short cognitive screening in elderlies as a part of advanced pharmaceutical care in Slovak community pharmacies - The pilot study KOGNIMET-SK
Bibliographic record
Abstract
Abstract The aim of this study is to evaluate the implementation of a simple, easy-to-use cognitive screening test in the pharmaceutical care of elderly patients (aged 60 years and over) in community pharmacies and to test whether cognitive decline is associated with metabolic syndrome (MetS). Current research has reported the increasing prevalence of MetS with age and indicated a link between cardiovascular risk factors and impairment of cognitive functions. We hypothesized that the short cognitive test realised in pharmaceutical care in community pharmacies could help identify risk groups of patients with potential cognitive decline. Because of the increasing pressure on primary care, collaboration between community pharmacists and general practitioners is essential, especially in preventive programmes and regarding chronically ill patients. The study included 222 elderly patients aged 60 years and over reached between February 2018 and February 2019 in 16 community pharmacies in Slovakia. Criteria for the classification of MetS were used, following the International Diabetes Federation Worldwide Definition of MetS (2005) for European populations. Cognitive performance was evaluated using the Slovak version of the Montreal Cognitive Assessment. The scale range of the MoCA test is 0–30 points, and the cut-off for cognitive impairment is ≤ 24 points. Our outcomes show that the presence of metabolic syndrome only slightly contributes to the impairment of cognitive functions of patients receiving routine pharmaceutical care in community pharmacies. Cognitive assessment might be an important tool for identifying risk groups of patients that would benefit from a specific approach within the framework of pharmaceutical care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".