Patient preferences for ongoing warfarin management after receiving care by an anticoagulation management service
Bibliographic record
Abstract
Optimal management of warfarin therapy in North America is achieved through anticoagulation management services (AMSs).1 These services typically operate at maximum capacity, manage patients until therapy is complete (which is often lifelong), and do not have the ability to care for all patients that could benefit from their services.2 Heneghan et al.3 suggest that patients in Europe who manage their own warfarin therapy achieve similar anticoagulant control, though the practice of self-management of warfarin therapy is virtually nonexistent in North America. We conducted a study to assess patient preferences and willingness to explore alternate management strategies after stabilization of therapy in an AMS. A telephone survey of a random sample of 75 patients receiving care from our ambulatory AMS for at least four months was conducted. A single interviewer external to the AMS used standardized text to provide an overview of potential future management strategies and to assess preferred choices and willingness to pursue the following options for their future care: continue with AMS care, referral back to their primary care physician, use point-of-care (POC) technology for self-testing, or manage their own therapy. For those willing to pursue either self-testing or self-management, preference between POC and venipuncture sampling was assessed, as were opinions surrounding third-party coverage and willingness to pay for the necessary technology.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".