The family nurse practitioner role in improving health care for the person over 70 years of age with chronic nonvalvular atrial fibrillation: the exploration of anticoagulation therapy
Bibliographic record
Abstract
The purpose of this project was to answer the question: Is the administration of warfarin the best practice for prevention of stroke in the person who is over the age of 70 years, who has nonvalvular atrial fibrillation (NVAF), and who is at high risk for having a stroke? A comprehensive literature review was conducted to help answer the primary research question. The findings of this review demonstrated that the administration of warfarin therapy was the best practice for the prevention of strokes in high-risk populations. The literature presented in this project further identified that although research findings support the use of warfarin therapy as best practice for prevention of strokes in the high-risk population, warfarin therapy was highly underutilized in general practice or has been managed subtherapeutically. Eight factors that may have affected the prescribing practices or suboptimal use of warfarin therapy by the health care practitioners were discussed. The project also explored how nurse practitioners can offer solutions for optimizing warfarin therapy in NVAF patients and concluded with recommendations for how nurse practitioners may help optimize treatment for patients with NVAF. --P.2.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".