Venous Thromboembolism Prophylaxis and Treatment in Patients With Cancer: ASCO Guideline Update
Bibliographic record
Abstract
PURPOSE: To conduct an update of the ASCO venous thromboembolism (VTE) guideline. METHODS: After publication of potentially practice-changing clinical trials, identified through ASCO's signals approach to updating, an updated systematic review was performed for two guideline questions: perioperative thromboprophylaxis and treatment of VTE. PubMed and the Cochrane Library were searched for randomized controlled trials (RCTs) published between November 1, 2018, and June 6, 2022. RESULTS: Five RCTs provided information that contributed to changes to the 2019 recommendations. Two RCTs addressed direct factor Xa inhibitors (either rivaroxaban or apixaban) for extended thromboprophylaxis after surgery. Each of these postoperative trials had important limitations but suggested that these two oral anticoagulants are safe and effective in the settings studied. An additional three RCTs addressed apixaban in the setting of VTE treatment. Apixaban was effective in reducing the risk of recurrent VTE, with a low risk of major bleeding. RECOMMENDATIONS: Apixaban and rivaroxaban were added as options for extended pharmacologic thromboprophylaxis after cancer surgery, with a weak strength of recommendation. Apixaban was also added as an option for the treatment of VTE, with high quality of evidence and a strong recommendation.Additional information is available at www.asco.org/supportive-care-guidelines.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".