Venous Thromboembolism Prophylaxis and Treatment in Patients With Cancer: ASCO Clinical Practice Guideline Update
Bibliographic record
Abstract
PURPOSE: To provide updated recommendations about prophylaxis and treatment of venous thromboembolism (VTE) in patients with cancer. METHODS: PubMed and the Cochrane Library were searched for randomized controlled trials (RCTs) and meta-analyses of RCTs published from August 1, 2014, through December 4, 2018. ASCO convened an Expert Panel to review the evidence and revise previous recommendations as needed. RESULTS: The systematic review included 35 publications on VTE prophylaxis and treatment and 18 publications on VTE risk assessment. Two RCTs of direct oral anticoagulants (DOACs) for the treatment of VTE in patients with cancer reported that edoxaban and rivaroxaban are effective but are linked with a higher risk of bleeding compared with low-molecular-weight heparin (LMWH) in patients with GI and potentially genitourinary cancers. Two additional RCTs reported on DOACs for thromboprophylaxis in ambulatory patients with cancer at increased risk of VTE. RECOMMENDATIONS: Changes to previous recommendations: Clinicians may offer thromboprophylaxis with apixaban, rivaroxaban, or LMWH to selected high-risk outpatients with cancer; rivaroxaban and edoxaban have been added as options for VTE treatment; patients with brain metastases are now addressed in the VTE treatment section; and the recommendation regarding long-term postoperative LMWH has been expanded. Re-affirmed recommendations: Most hospitalized patients with cancer and an acute medical condition require thromboprophylaxis throughout hospitalization. Thromboprophylaxis is not routinely recommended for all outpatients with cancer. Patients undergoing major cancer surgery should receive prophylaxis starting before surgery and continuing for at least 7 to 10 days. Patients with cancer should be periodically assessed for VTE risk, and oncology professionals should provide patient education about the signs and symptoms of VTE.Additional information is available at www.asco.org/supportive-care-guidelines.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".