P3610Predictors of warfarin discontinuation or switching among non-valvular atrial fibrillation patients
Bibliographic record
Abstract
Background: Warfarin has been widely used for stroke prevention among patients with non-valvular atrial fibrillation (NVAF). Discontinuation or switching from warfarin therapy is common among NVAF patients due to issues such as risk of bleeding, a narrow therapeutic window, drug and food interactions, and frequent INR monitoring. Purpose: To evaluate the extent and predictors of warfarin discontinuation or switching among NVAF patients newly treated with warfarin. Methods: A retrospective study of NVAF patients newly initiating warfarin from 01 November 2011 to 30 September 2015 was conducted using data from the U.S. Department of Defense database. Warfarin discontinuation was defined as not having a warfarin prescription refilled within 60 days after the end of the previous prescription, and without INR monitoring at least every 42 days. Warfarin switching was defined as having ≥1 prescription filled for non-vitamin K antagonist oral anticoagulants (NOACs), including apixaban, dabigatran, rivaroxaban, or edoxaban, within 60 days before or after the warfarin discontinuation date. Cox proportional hazards models with time-dependent covariates (major bleeding and stroke/systemic embolism (SE) hospitalizations) were used to evaluate the predictors of warfarin discontinuation or switching versus continuous warfarin use.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".