Use and Assessment of Knowledge of Vitamin K Antagonist Therapy in Cardiac Patients
Bibliographic record
Abstract
Background: Safety and efficacy of Vitamin K antagonists (VKAs), the most widely used oral anticoagulant (OAC), is monitored by therapeutic international normalized ratio (INR). The current study was conducted to evaluate the proportion of patients achieving therapeutic range INR and assessment of the knowledge, and awareness among patients regarding OAC therapy, as well as identification of the challenges in the monitoring of INR. Materials and Methods: This hospital-based, single-center cross-sectional study was conducted at a tertiary care hospital in Delhi. Patients on anticoagulation with VKAs were interviewed and their records were reviewed. Information on sociodemographic characteristics, history of cardiac illness, INR range, knowledge, and awareness regarding VKA therapy were analyzed. Data management was done via CSPro and statistical analysis via STATA 13.0. Results: A total of 86 patients were evaluated. The mean age of the study participants was 49 ± 14.9 years. Only 29.1% of the study group achieved therapeutic INR. Overall awareness and knowledge regarding the need for VKA therapy, ideal INR range, complications of poor monitoring, and dietary restrictions were in the range of 31%–48%. Conclusion: Poor INR control is prevalent in Indian patients on VKAs therapy. Although the future practice may move toward newer anticoagulants, a substantial proportion of our population may still need VKAs. Hence, there is a need for improving the knowledge and awareness of patients on VKA therapy to improve therapeutic effectiveness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".