Direct Oral Anticoagulants in the Real World
Bibliographic record
Abstract
Background Direct oral anticoagulants are convenient because of their fixed dosing and without laboratory monitoring. There are instructions on avoidance of moisture, no crushing of capsules, and administration with food for some direct oral anticoagulants. Whether patients adhere to this and are prescribed appropriate doses are unknown. Aims To assess direct oral anticoagulant dosing and medication use. Methods Patients ≥18 years old, receiving a direct oral anticoagulant for any diagnosis, were prospectively included. Nurses at our perioperative anticoagulation clinic helped patients complete a 12-item questionnaire. Results Ninety-three consecutive patients were recruited. Forty-nine were on dabigatran, 18 on apixaban, and 26 were on rivaroxaban. Sixty-two patients (67%) received appropriate direct oral anticoagulant dosing and administered the medication correctly. Eighteen patients (19%) administered the direct oral anticoagulant properly but at an inappropriate dose. Thirteen patients (14%) received an appropriate dose but administered the direct oral anticoagulant inappropriately: 10 (11%) removed dabigatran from its packaging before administration (exposing it to moisture); 2 (2%) did not take rivaroxaban with food; and 1 (1%) crushed the dabigatran capsule. Conclusion Our study demonstrates a large variability in how direct oral anticoagulants are dosed, and how patients take them. Improved medication literacy around direct oral anticoagulants is needed. Our study highlights opportunities that nurses have to improve patients' medication literacy.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".