A Simple Warfarin Dosing Nomogram for Orthopedic Prophylaxis
Bibliographic record
Abstract
INTRODUCTION Total hip replacement procedures are considered high-risk surgery, for which the prevalence of deep vein thrombosis is 45% to 57%, of pumonary embolism 6.7% to 30%, and of fatal pulmonary embolism 3.4% to 6%.1,2 General recommendations for prophylaxis of deep vein thrombosis in patients who have undergone this type of surgery include early mobilization, intermittent pneumatic compression, elastic stockings, and early detection of subclinical venous thrombosis by duplex ultrasonography or venography.1 The pharmacological options for preventing deep vein thrombosis include low-molecular-weight heparin, low-intensity warfarin, and adjusted-dose subcutaneous heparin.1,2 Because low-molecular-weight heparin was not a formulary drug at the time this study was initiated (in November 1995) and subcutaneous administration of adjusted-dose heparin is labour intensive, warfarin has been used as the prophylactic agent at our institution. Our pharmacy department was asked by the Division of Orthopedics to provide a warfarin dosing service for prophylaxis of deep vein thrombosis in patients requiring hip arthroplasty. Before this request was made, the pharmacy department had been involved in creating and implementing a weight-based heparin dosing nomogram.3 This nomogram, which was run by the nursing staff, was found to be superior to traditional heparin dosing for rapidly and safely achieving therapeutic anticoagulation. Because of the success of this program, it was felt that a warfarin dosing service based on a nomogram could provide a similar success rate and also improve patient care. Pharmacists were chosen to run this service because of the complex pharmacokinetics of warfarin and the numerous factors affecting the anticoagulant response that must be considered before deciding on dosage adjustments. At the time of this request, orthopedic residents were responsible for prescribing warfarin. The residents were often unavailable because of prolonged surgeries or inadequate weekend coverage, which resulted in late administration of warfarin and frustration on the part of the nursing staff.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".