Evaluation of a community-based memory clinic in collaboration with local hospitals to support patients with memory decline
Bibliographic record
Abstract
Objective: This study evaluates the role of a specialised and multidisciplinary healthcare team, including a pharmacist, in providing medication management for patients with mild cognitive impairment (MCI) and dementia, in a memory clinic. Methods: The study analysed the dataset of 102 patients of a geriatric and memory clinic in a rural area of Ontario, Canada. The case histories of the patients were reviewed a week before the clinic day and a pharmacist performed medication reconciliations. During the clinic day, cognitive tests were conducted and outcomes were discussed with the team, to create a care plan and schedule a follow-up within 3, 6 or 12 months. Results: -value 0.001) were deprescribed, with 510 prescriptions and 202 non-prescription items. Out of the 712 deprescribed drugs, 374 were discontinued with no therapeutic substitutions, 202 were reduced in dosage and 136 were switched to a safer alternative. A total of 43 patients showed improved Activities of Daily Living (ADL) performance after 3 and 6 months and 68 patients showed improvement after 12 months. Conclusion: This study highlights the importance of a multidisciplinary approach in addressing drug-therapy problems, medication optimisation, and deprescription in patients with dementia. The presence of a pharmacist in the multidisciplinary team enables impactful medication optimisation and leads to improved patient outcomes. This demonstrates the value of specialised expertise in medication management for patients with dementia.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".