ABO Blood Group and the Risk of Thrombosis in Cancer Patients: A Mini-Review
Bibliographic record
Abstract
Cancer-associated thrombosis (CT), especially venous thromboembolism (VTE), is a common occurrence with several factors contributing to a wide diversity in thrombosis risk. The association between ABO blood groups and the risk for CT has been examined in various studies, with non-O blood type associated with an increased thrombosis risk; however, these studies have reported varying results with recognized limitations. ABO blood groups are known to be implicated in hemostasis, in an association mediated through von Willebrand factor (VWF). In this narrative review, we aim to summarize the current knowledge surrounding the role of ABO blood groups in VTE, with a particular focus on the role of VWF and other contributing risk factors on VTE occurrence. We found evidence from literature for the impact of ABO blood groups in determining the risk of VTE in healthy populations, with a limited number of studies examining this effect in cancer patients. Additionally, research on the impact of ABO on different cancer types lacks rigor, particularly in regard to other risk factors. Overall, most studies showed strong association of increased risk of VTE amongst cancer patients with non-O blood groups and increased VWF levels. This association was weaker in a few studies. Further research is needed before a solid conclusion can be made about the ABO or ABO-VWF-mediated hypercoagulability and VTE risk in various cancers. These studies will help determine if ABO typing can be an added biomarker to improve VTE risk assessment models in cancer patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".