Enhancing Patient Care via a Pharmacist-Managed Rural Anticoagulation Clinic
Bibliographic record
Abstract
Integrating specialized pharmacist services and follow-up with the laboratory, home care nursing, retail pharmacy and physicians can ensure optimal outcomes for patients receiving anticoagulation, or "blood thinner," therapy. Improved patient education and discharge care planning can bridge disconnects, enable patients to better manage their care and ensure better patient outcomes and more effective use of health system resources. Specially trained pharmacists can provide safe and effective management of a high-alert medication to help prevent potentially life-threatening clots or bleeding. With advanced prescribing authorization, the pharmacist can seamlessly provide this service both locally in a community and via Telehealth to surrounding areas, potentially for any Albertan. Warfarin therapy may be lifelong or short-term (three to six months), but all patients require regular monitoring with blood tests. Many variables, both lifestyle and medication related, can impact therapy, and through extensive education and access via telephone to an "expert" for questions and follow-up of blood tests, patients are empowered to better regulate their anticoagulants. Anticoagulation pharmacists, as part of an AMS (anticoagulation management service), can provide a continuum of care for patients while in hospital, when discharged home, as an outpatient in the community or as a resident of a long-term care facility or seniors' home.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".