Bibliographic record
Abstract
For over 60 years, warfarin has been the treatment of choice in the prevention of strokes and other thromboembolic events. In recent years, a new class of Novel Oral Anticoagulant (NOAC) medication has become available, leaving clinicians and health system payors to question whether warfarin continues to have a place in therapy. This article argues that it may not be the medication that should be in question but instead the systems in place to manage anticoagulation for the patients who need it. Usual Care (UC) for warfarin management has traditionally required multiple healthcare visits, blood collection visits, and laboratory analysis of International Normalized Ratio (INR) with results to then later be relayed to the patient along with dosage adjustments. The article reviews a new model of care, Community Pharmacist-led Anticoagulation Management Service (CPAMS), in which patients receive a point-of-care INR test along with a pharmacist assessment at a pharmacy and results within minutes. Pharmacists then prescribe dosage adjustments immediately, counsel patients, and provide supporting adherence tools such as a colourful picture-based dosing calendar, created by the decision support tool, INR Online. The Nova Scotia CPAMS Demonstration Project shows that this model will result in efficiencies for healthcare providers and optimal anticoagulation with improved time in therapeutic range outcomes for patients. In addition, the CPAMS Costing Study finds the model to be a cost-effective solution for health systems when compared to UC for warfarin as well as NOAC patients.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".