Study protocol for identification of patients with risk of cognitive impairment in advanced pharmaceutical care in a community pharmacy
Bibliographic record
Abstract
Introduction: Cognitive impairment (CI) is a growing public health problem. Our study is based on the fact that cognitive assessment in community pharmacy focused on early identification of undiagnosed CI has received limited attention. As pharmacists are the most accessible health professionals due to the availability of community pharmacies to the public, they have the potential to bring improvement in this area. Early identification of at-risk patients with CI by performing cognitive testing within advanced pharmaceutical care may improve the availability of targeted physician-indicated treatment. Methods and analysis: The study is a multicentric study that will include cognitive screening within pharmaceutical care. We will use the Slovak version of the short form of the Montreal Cognitive Assessment (s-MoCA) test. Study participants will be at-risk patients undergoing cognitive screening in community pharmacies. Secondarily, we will evaluate the risk factors related to CI, such as at-risk medication use and modifiable dementia risk factors (e.g., cardiovascular and mental comorbidities, aging, and lifestyle habits). Ethics and dissemination: This study was approved by the Ethics Committee of the Faculty of Pharmacy, Comenius University Bratislava (Ethics Committee Statement 01/2024). All procedures follow the relevant guidelines and regulations and the Declaration of Helsinki.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.085 | 0.018 |
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".