Approach to the new oral anticoagulants in family practice: part 2: addressing frequently asked questions.
Bibliographic record
Abstract
OBJECTIVE: To address common "what if" questions that arise relating to the long-term clinical follow-up and management of patients receiving the new oral anticoagulants (NOACs). SOURCES OF INFORMATION: For this narrative review, we searched the PubMed database for recent (January 2008 to week 32 of 2013) clinical studies relating to NOAC use for stroke prevention in atrial fibrillation and for the treatment of acute venous thromboembolism. We used this evidence base to address prespecified questions relating to NOAC use in primary care settings. MAIN MESSAGE: Dabigatran and rivaroxaban should be taken with meals to decrease dyspepsia and increase absorption, respectively. There are no dietary restrictions with any of the NOACs, beyond moderating alcohol intake, and rivaroxaban and apixaban can be crushed if required. The use of acid suppressive therapies does not appear to affect the efficacy of the NOACs. As with warfarin, patients taking NOACs should avoid long-term use of nonsteroidal anti-inflammatory and antiplatelet drugs. For patients requiring surgery, generally NOACs should be stopped 2 to 5 days before the procedure, depending on bleeding risk, and the NOAC should usually be resumed at least 24 hours after surgery. Preoperative coagulation testing is generally unnecessary. In patients who develop bleeding, minor bleeding typically does not require laboratory testing or discontinuation of NOACs; with major bleeding, the focus should be on local measures to control the bleeding and supportive care, and coagulation testing should be performed. There are currently no antidotes to reverse NOACs. The NOACs should not be used in patients with valvular heart disease, prosthetic heart valves, cancer-associated deep vein thrombosis, or superficial thrombophlebitis. CONCLUSION: Management of "what if" scenarios for patients taking NOACs have been proposed, but additional study is needed to address these issues, especially periprocedural management and bleeding.
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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 | 0.025 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".