American Society of Hematology 2018 guidelines for management of venous thromboembolism: optimal management of anticoagulation therapy
Bibliographic record
Abstract
Abstract In July 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: Clinicians confront numerous practical issues in optimizing the use of anticoagulants to treat venous thromboembolism (VTE). Objective: These evidence-based guidelines of the American Society of Hematology (ASH) are intended to support patients, clinicians and other health care professionals in their decisions about the use of anticoagulants in the management of VTE. These guidelines assume the choice of anticoagulant has already been made. 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 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 25 recommendations and 2 good practice statements to optimize management of patients receiving anticoagulants. Conclusions: Strong recommendations included using patient self-management of international normalized ratio (INR) with home point-of-care INR monitoring for vitamin K antagonist therapy and against using periprocedural low-molecular-weight heparin (LMWH) bridging therapy. Conditional recommendations included basing treatment dosing of LMWH on actual body weight, not using anti–factor Xa monitoring to guide LMWH dosing, using specialized anticoagulation management services, and resuming anticoagulation after episodes of life-threatening bleeding.
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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".