Emerging antithrombotic agents for thromboprophylaxis, clinical potential and patient considerations
Bibliographic record
Abstract
Patients undergoing major orthopedic surgery, total hip arthroplasty (THA) and total knee arthroplasty (TKA) are at high risk of venous thromboembolism, manifesting as deep vein thrombosis or pulmonary embolism. The recommended pharmacologic treatment options for thromboprophylaxis after major orthopedic surgery include the vitamin K antagonists (VKAs eg, warfarin), low molecular weight heparins (LMWHs; eg, enoxaparin) and the synthetic pentasaccharide fondaparinux. Most clinics use some kind of thromboprophylaxis routinely. However, due to the frequent need for coagulation monitoring (VKAs) and subcutaneous injections (LMWHs and fondaparinux) barriers exist to prescribing prophylaxis after discharge from hospital. Targeting specific components of the coagulation cascade has yielded several new antithrombotic agents for use as thromboprophylaxis after THA or TKA. Two of these, dabigatran etexilate and rivaroxaban, have already reached the markets in the European Union member states and Canada. Both are administered by the oral route, once-daily fixed dose and without the need to monitor the anticoagulant effect. Whether these new drugs facilitate guideline adherence, particularly in the outpatient settings and thereby improve the overall clinical outcomes remains to be shown.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".