Anticoagulation stewardship: Descriptive analysis of a novel approach to appropriate anticoagulant prescription
Bibliographic record
Abstract
Background: Anticoagulants are a leading cause of morbidity among hospitalized patients, with prescription errors commonly reported. Literature surrounding anticoagulation stewardship is scarce despite its documented effectiveness in the antimicrobial realm. Objective: To determine the proportion of accepted recommendations on inappropriate anticoagulant prescriptions suggested by a multidisciplinary anticoagulation stewardship program (ASP). Methods: We conducted a descriptive cohort study of hospitalized patients using therapeutic anticoagulation at a large Canadian tertiary care center between September 1, 2019, and February 28, 2020. A multidisciplinary ASP, composed of physicians and pharmacists, was implemented on June 1, 2019. Patient-, anticoagulant-, and admission-related characteristics were collected. The primary outcome was the proportion of accepted ASP team recommendations by the prescribing team. Results: A total of 381 patients were enrolled during the study period, resulting in 553 anticoagulant reviews (1.56 reviews/patient) by the ASP. The most common indications for anticoagulation were atrial fibrillation (n = 276, 72%) and venous thromboembolism (n = 84, 22%). Direct oral anticoagulants were most frequently prescribed (n = 253, 67%), followed by vitamin K antagonists (n = 88, 23%). Among the reviewed prescriptions, 355 of 553 (64%) generated a recommendation; 299 of 355 (84%) recommendations were accepted by the treating team. Dose adjustments were the leading category of recommendations (31%), followed by alerts regarding drug interactions (19%). Conclusion: Inpatient anticoagulant prescriptions were optimized following recommendations by the ASP team. The most frequent types of prescription changes concerned dose adjustments and drug interactions. Further research is required to assess the effect of an ASP on clinical outcomes.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".