Bibliographic record
Abstract
Pharmacists have developed innovative practices in various settings as singular providers or as members of multidisciplinary or interdisciplinary teams. Examples include pharmacists practicing in heart failure, hypertension, or hyperlipidemia clinics. There is a paucity of literature describing pharmacists in interdisciplinary memory clinics and specifically pharmacists practicing in interdisciplinary, primary care-based memory clinics. New practice models should be disseminated to guide others in the development of similar models given the complexity of this population. Patients with dementia are more difficult to manage because of cognitive impairment, behavioral and psychological symptoms, the common presence of multiple comorbidities, and related polypharmacy and caregiver issues. These challenges require expertise in neurodegenerative disorders and geriatrics. The purpose of this article is to describe the role of clinical pharmacists providing care to patients with cognitive complaints in a primary care-based, interdisciplinary memory clinic, with a focus on how the pharmacist practices and is integrated in this collaborative care setting. Patients are assessed using an interdisciplinary approach, with team consensus for assessment and planning of care. Pharmacists' activities include assessment of (1) appropriateness of medications based on frailty, (2) medications that can impair cognition and/or function, (3) medication adherence and management skills, and (4) vascular risk factor control. Pharmacists provide education regarding medications and diseases, ensure appropriate transitions in care, and conduct home visits. Pharmacist participation in this clinic represents a novel opportunity to advance pharmacy practice in primary care, interdisciplinary models. Work is ongoing to describe outcomes attributable to pharmacist participation in this clinic.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".