Best evidence summary on anticoagulant management in patients with cancer-associated venous thromboembolism
Bibliographic record
Abstract
Objective: To retrieve, evaluate, and summarize the best evidence for anticoagulant management in patients with cancer-associated venous thromboembolism (VTE). Methods: This was an evidence summary study conducted in strict accordance with the reporting standards of the Fudan University Center for Evidence-Based Nursing. According to the evidence pyramid "6S" model, the search was performed from top to bottom. The following databases were searched: BMJ Best Practice, UpToDate, Joanna Briggs Institute, Cochrane Library, Guidelines International Network, National Institute for Health and Clinical Excellence, Scottish Intercollegiate Guidelines Network, Registered Nurses Association of Ontario, YiMaiTong Guidelines Network, PubMed, Embase, Web of Science, Sinomed, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP. Professional association websites included those of the American Society of Clinical Oncology, American Society of Hematology, British Society for Haematology, and the Scientific and Standardization Committee of the International Society on Thrombosis and Haemostasis. The search period was from the inception of each database to October 2024. Literature types included clinical practice guidelines, clinical decisions, expert consensuses, systematic reviews, and randomized controlled trials (RCTs). Results: A total of 19 studies were included, comprising three clinical decisions, 10 guidelines, one expert consensus, four systematic reviews, and one RCT. Thirty-four pieces of evidence were synthesized across eight aspects: risk factor assessment, timing of anticoagulation, drug selection, treatment duration, management of recurrence, safety monitoring, health education, and follow-up care. Conclusions: This study summarizes the best evidence for anticoagulant management in patients with cancer-associated VTE. When applying this evidence clinically, health care providers should thoroughly evaluate the feasibility and appropriateness of each recommendation to develop clinical decisions tailored to China's national conditions and health care resources. Systematic review registration: This study has been registered on the Fudan University Centre for Evidence-based Nursing (Registration No. ES20244015).
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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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".