Pharmacoeconomic evaluation of direct oral anticoagulants for cancer-associated thrombosis: a systematic review
Bibliographic record
Abstract
Objective: To synthesize pharmacoeconomic evidence of prevention and treatment of venous thromboembolism (VTE) in cancer patients with direct oral anticoagulants (DOACs) and evaluate the quality of the studies. Methods: PubMed, Embase, Scopus, the Cochrane Library, the Center for Reviews and Dissemination Database, the Health Technology Assessment Database, and the China National Knowledge Infrastructure Database were searched to collect economic evaluations. The search covered publications from their inception until June 13, 2024. Study selection was conducted independently by two researchers, with discrepancies resolved through discussion. The quality of the studies were assessed using the Consolidated Health Economic Evaluation Reporting Standards 2022 checklist, and the basic characteristics of the included studies were summarized descriptively. Results: A total of 15 studies were included, covering different income level countries: the United States, Spain, China, the Netherlands, Canada, and Brazil. Economic evaluation results for prevention strategies varied in different countries. The baseline VTE incidence and drug costs will determine whether DOACs are worthwhile. For the treatment of VTE in cancer patients, DOACs were found to be more cost-effective compared to low molecular weight heparins (LMWHs) and warfarin, though the incremental cost-effectiveness ratio varied significantly across countries. However, there is still a lack of pharmacoeconomic studies based on direct evidence on which DOAC to choose for VTE treatment in cancer patients. Conclusion: The cost-effectiveness of DOACs for VTE in cancer patients has been proven. Further research is needed to determine the best choice of DOAC. Thromboprophylaxis in all cancer patients is not recommended. It is still necessary for clinicians to evaluate the risk of VTE. Pharmacoeconomic study results are significantly influenced by the drug costs, patient preferences, and income levels of different countries and regions. Economic decisions should be made according to the specific national background.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".