Bibliographic record
Abstract
I read and appreciated the recent article by Marc Riachi1 discussing various aspects of how pharmacists might improve their interaction with cognitively impaired patients, in light of his own personal experience as a caregiver. It was very timely given the increasing numbers of seniors in Canada. I have no personal experience regarding caring for a family member with any form of dementia, but I do work in a clinic setting where many patients present with cognitive issues, and I am myself a senior citizen. I am increasingly convinced that pharmacists should do much more to provide specialty services for the elderly population in general and for cognitively impaired persons in particular, perhaps even expanding their scope of practice to include cognitive testing and subsequent referral to a family physician or geriatric specialist. One example of such a test can be found at www.alz.org/documents_custom/141209-CognitiveAssessmentToo-kit-final.pdf. With appropriate training, such a concise cognitive assessment could be administered with patient consent, perhaps as part of the annual medication review initiative. It has been my experience that pharmacists have a particularly useful linear relationship with many patients and their caregivers over time, which would be of particular value in the recording of changes in cognitive function.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".