Anticoagulation Stewardship to Bridge the Implementation Gap in Perioperative Anticoagulation Management
Bibliographic record
Abstract
Lack of alignment of care protocols among providers in health care is a driver of increased costs and suboptimal patient outcomes. Perioperative anticoagulation management is a good example of a complex area where protocol creation is a clinical challenge that demands input from multiple experts. Questions regarding the need for anticoagulation interruptions are frequent. Yet, due to layers of complexity involving analysis of anticoagulation indication, surgical risk, and anesthesia-associated bleeding risk as well as institutional practices, there is heterogeneity in how these interruptions are approached. The recent perioperative anticoagulation guidelines from the American College of Chest Physicians summarize extensive evidence for the management of anticoagulant and antiplatelet medications in patients who undergo elective interventions. However, implementation of these guidelines by individual clinicians is highly varied and often does not follow the best available clinical evidence. Against this background, anticoagulation stewardship units, which exist to improve safety and quality monitoring for the anticoagulated patient, are of growing interest. These units provide a bridge for the implementation of value-based, high-quality guidelines for patients who need perioperative anticoagulation interruption. We use a case to pragmatically illustrate the problem and tactics for change management and implementation science that may facilitate the adoption of perioperative anticoagulation guidelines.
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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 | 0.089 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.015 | 0.027 |
| 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".