Guidance for the treatment of deep vein thrombosis and pulmonary embolism
Bibliographic record
Abstract
This guidance document focuses on the diagnosis and treatment of venous thromboembolism (VTE). Efficient, cost effective diagnosis of VTE is facilitated by combining medical history and physical examination with pre-test probability models, D dimer testing and selective use of confirmatory imaging. Clinical prediction rules, biomarkers and imaging can be used to tailor therapy to disease severity. Anticoagulation options for acute VTE include unfractionated heparin, low molecular weight heparin, fondaparinux and the direct oral anticoagulants (DOACs). DOACs are as effective as conventional therapy with LMWH and vitamin K antagonists. Thrombolytic therapy is reserved for massive pulmonary embolism (PE) or extensive deep vein thrombosis (DVT). Inferior vena cava filters are reserved for patients with acute VTE and contraindications to anticoagulation. Retrievable filters are strongly preferred. The possibility of thoracic outlet syndrome and May-Thurner syndrome should be considered in patients with subclavian/axillary and left common iliac vein DVT, respectively in absence of identifiable triggers. The optimal duration of therapy is dictated by the presence of modifiable thrombotic risk factors. Long term anticoagulation should be considered in patients with unprovoked VTE as well as persistent prothrombotic risk factors such as cancer. Short-term therapy is sufficient for most patients with VTE associated with transient situational triggers such as major surgery. Biomarkers such as D dimer and risk assessment models such the Vienna risk prediction model offer the potential to customize VTE therapy for the individual patient. Insufficient data exist to support the integration of bleeding risk models into duration of therapy planning.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.039 |
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".