Preoperative Management of Antithrombotics in Arthroplasty
Bibliographic record
Abstract
Antithrombotic therapy is common in the arthroplasty patient population; the preoperative management of chronic antithrombotic medications requires coordination among the medical team. It is estimated that approximately 250,000 or 10% of patients on chronic antithrombotic medication undergo treatment interruption for surgical procedures annually in North America. Although the description of postoperative anticoagulation management after arthroplasty is extensive, orthopaedic literature describing the preoperative management of antithrombotic therapy is lacking. The goal of this guideline is to provide practicing orthopaedic surgeons concise recommendations for the preoperative management of common contemporary antithrombotics in the setting of elective arthroplasty using evidence-based guidelines from other medical specialties. All arthroplasty procedures are considered high bleeding risk in accordance with collaborative AAOS and ACC guidelines. Orthopaedic surgeons should collaborate with their colleagues in cardiology, anesthesia, and other specialties when planning perioperative antithrombotic interruption, particularly in the case of medically complex patients such as those with known risk factors for bleeding and clotting disorders. Resumption of antithrombotic therapy after arthroplasty is beyond the scope of this discussion; this should be performed in accordance with cardiology and anesthesia recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".