American Society of Hematology 2020 Guidelines for Management of Venous Thromboembolism: Treatment of Deep Vein Thrombosis and Pulmonary Embolism
Bibliographic record
Abstract
Abstract In August 2022, these guidelines were reviewed by an expert work group convened by ASH. Review included limited searches for new evidence and discussion of the search results. Following this review, the ASH Committee on Quality agreed to continue monitoring the supporting evidence rather than revise or retire these guidelines at this time. Limited searches and expert review will be repeated annually going forward until these guidelines are revised or retired. Background: Venous thromboembolism (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), occurs in ∼1 to 2 individuals per 1000 each year, corresponding to ∼300 000 to 600 000 events in the United States annually. Objective: These evidence-based guidelines from the American Society of Hematology (ASH) intend to support patients, clinicians, and others in decisions about treatment of VTE. Methods: ASH formed a multidisciplinary guideline panel balanced to minimize potential bias from conflicts of interest. The McMaster University GRADE Centre supported the guideline development process, including updating or performing systematic evidence reviews. The panel prioritized clinical questions and outcomes according to their importance for clinicians and adult patients. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to assess evidence and make recommendations, which were subject to public comment. Results: The panel agreed on 28 recommendations for the initial management of VTE, primary treatment, secondary prevention, and treatment of recurrent VTE events. Conclusions: Strong recommendations include the use of thrombolytic therapy for patients with PE and hemodynamic compromise, use of an international normalized ratio (INR) range of 2.0 to 3.0 over a lower INR range for patients with VTE who use a vitamin K antagonist (VKA) for secondary prevention, and use of indefinite anticoagulation for patients with recurrent unprovoked VTE. Conditional recommendations include the preference for home treatment over hospital-based treatment for uncomplicated DVT and PE at low risk for complications and a preference for direct oral anticoagulants over VKA for primary treatment of VTE.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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".