Association Between Patient Knowledge of Anticoagulation, INR Control, and Warfarin-Related Adverse Events
Bibliographic record
Abstract
Background: Whether the level of patient’s knowledge about warfarin plays any role in maintenance of therapeutic international normalized ratio (INR) is controversial. Several studies have looked at patients’ warfarin knowledge and the level of patients’ anticoagulation control (AC). Most studies had small numbers and did not use validated questionnaires. Objectives: To use the Oral Anticoagulation Knowledge (OAK) test to assess patients’ knowledge of AC and to examine associations between knowledge, INR, and adverse events. Methods: In this cross-sectional study, patients were asked to complete the OAK test. Data on clinical and demographic characteristics, INR values, and thrombosis or bleeding events during the preceding 1 year period were collected. Associations between OAK scores, patient characteristics, proportion of therapeutic INRs, and bleeding/thrombosis events were assessed. Results: A total of 225 patients completed the OAK test. Mean (SD) age was 70 (13.4) years, 53% were male, and 75% were on warfarin for >3 years. Over the preceding year, 57.3% of INRs were therapeutic, and there were 22 bleeding and 6 thrombotic events. The mean OAK score was 12/20 (passing score = 15/20); 64% of patients failed the OAK test. Predictors of passing the OAK test were younger age ( P = .01) and higher level of education ( P = .03). There was no association between OAK score and proportion of therapeutic INRs, or OAK score and bleeding or thrombosis events. Conclusion: We used the OAK test to assess patients’ AC knowledge. Results suggests that while younger and more educated patients were more likely to pass the OAK test, the OAK test results may not predict INR control or occurrence of bleeding or thrombotic events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".