The diagnosis and treatment of venous thromboembolism
Bibliographic record
Abstract
Venous thromboembolism (VTE) is a common condition that can lead to complications such as postphlebitic syndrome, chronic pulmonary artery hypertension, and death. The approach to the diagnosis of has evolved over the years and an algorithm strategy combining pretest probability, D-dimer testing, and diagnostic imaging now allows for safe, convenient, and cost-effective investigation of patients. Patients with low pretest probability and a negative D-dimer can have VTE excluded without the need for imaging. The mainstay of treatment of VTE is anticoagulation, whereas interventions such as thrombolysis and inferior vena cava filters are reserved for special situations. Low-molecular-weight heparin has allowed for outpatient management of most patients with deep vein thrombosis at a considerable cost savings to the health care system. Patients with malignancy-associated VTE benefit from decreased recurrent rates if treated with long-term low-molecular-weight heparin. The development of new oral anticoagulants further simplifies treatment. The duration of anticoagulation is primarily influenced by underlying cause of the VTE (whether provoked or not) and consideration of the risk for major hemorrhage. Testing for genetic and acquired thrombophilia may provide insight as to the cause of a first idiopathic deep vein thrombosis, but the evidence linking most thrombophilias to an increased risk of recurrent thrombosis is limited.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".