Ten-Year Trends in Venous Thromboembolism Prophylaxis at a High-Volume Arthroplasty Center
Bibliographic record
Abstract
INTRODUCTION: Venous thromboembolism (VTE) prophylaxis is the standard of care after total joint arthroplasty. However, there have been changes in the prevalence of certain medication classes used by institutions over time driven by the literature and national clinical practice guidelines. The purpose of this study was to analyze the patterns of VTE medications over the past 10 years at our institution. METHODS: We identified 25,095 patients who underwent a primary total joint arthroplasty between 2012 and 2022. Medications for VTE prophylaxis included aspirin, warfarin, unfractioned heparin (UFH), low-molecular-weight heparin (LMWH), factor-Xa inhibitors (FXa), and antiplatelet agents different from aspirin and thrombin inhibitors. Tranexamic acid use was recorded. The rates of symptomatic VTE were calculated and categorized as deep vein thrombosis or pulmonary embolism. RESULTS: Venous thromboembolism rates decreased from 1.1% in 2012 to 0.2% in 2022 and ranged between 0.4% and 1.2% during the ten-year period. Although VTE incidence decreased in the past 4 years, an isolated increase was noted in 2021(1%). In 2012, the use of aspirin, warfarin, FXa, and UFH were 52.1%, 30.7%, 0%, and 16.2%, respectively, whereas in 2022, the rates for the same medications were 83.3%, 0.3%, 10.4%, and 1.8%, respectively. The use of LMWH, thrombin inhibitors, and other antiplatelet agents had minimal variation, and none of these medications surpassed 5% during this period. The aspirin dose changed from 325 mg in 2012 (96.5%) to 81 mg in 2022 (98.8%). Similarly, TXA use increased from 30.8% in 2012 to 84.9% in 2022. CONCLUSION: In the past 10 years, our institution has transitioned from warfarin, UFH, and high-dose aspirin to low-dose aspirin and FXa, exhibiting acceptably low deep vein thrombosis and diminishing pulmonary embolism rates during that time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".