Potential for a Virtual Care Model in the Perioperative Management of Anticoagulant Therapy: A 5-Year Retrospective Clinic Review
Bibliographic record
Abstract
Abstract Background With a trend toward greater virtual care in selected clinical settings, perioperative anticoagulant management appears well suited for this care delivery model. We explored the potential for virtual care among patients who are receiving anticoagulant therapy and require perioperative management around the time of an elective surgery/procedure. Methods We undertook a retrospective review of patients who were receiving anticoagulant therapy, either a direct oral anticoagulant (DOAC) or warfarin, assessed in a perioperative anticoagulation-bridging clinic over a 5-year period from 2016 to 2020. Using prespecified criteria, we determined the proportion of patients who likely would be suitable for virtual care (receiving a DOAC or warfarin and having a minimal- or low-/moderate-bleed-risk surgery/procedure), those who likely would be suitable for in-person care (receiving warfarin and requiring heparin bridging for a mechanical heart valve), and patients who would be suitable for either care delivery model (receiving a DOAC or warfarin, but not with a mechanical heart valve, and requiring a high-bleed-risk surgery/procedure). Results During the 5-year study period, there were 4,609 patients assessed for perioperative anticoagulant management in whom the most widely used anticoagulants were warfarin (37%), apixaban (30%), and rivaroxaban (24%). Within each year assessed, 4 to 20% of all patients were undergoing a minimal-bleed-risk procedure, 76 to 82% were undergoing a low-/moderate-bleed-risk surgery/procedure, and 10 to 39% were undergoing a high-bleed-risk surgery/procedure. The proportion of patients considered suitable for virtual, in-person, or either virtual or in-person management was 79.6, 7.1, and 13.3%, respectively. Conclusion In patients who were assessed in a perioperative anticoagulation clinic, there was a high proportion of patients in whom a virtual care model might be suitable.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".