Association Between Anticoagulant‐Related Bleeding and Mortality in Patients With Solid Tumors and Cancer‐Associated Venous Thromboembolism
Bibliographic record
Abstract
To the editor:Venous thromboembolism (VTE) is a significant cause of morbidity and mortality in cancer patients.Managing VTE in cancer patients involves balancing elevated risks of VTE recurrence with anticoagulant (AC)-related bleeding.Risk of AC-related bleeding is exacerbated in cancer patients due to older age, thrombocytopenia, frailty, comorbid disease, and tumor invasion [1].Limited data exist on the association between AC-related bleeding and survival in patients with cancer-associated VTE.In one meta-analysis of patients with cancer, the case fatality rate for AC-related major bleeding was nearly 1 in 10 patients [2].In another study, patients with cancer-associated VTE had a 2.7-fold increased rate of bleedingrelated mortality compared to patients with VTE without cancer [3].However, fatality in respect to site of bleed was not reported.This study aims to quantify the relationship between clinically significant bleeding events and death in patients with cancer-associated VTE newly initiated on AC therapy, stratified by site of bleeding.Utilizing data from the nationwide US Veterans Affairs (VA) healthcare system between 2012 and 2020, we retrospectively identified patients with solid tumor using international classification of diseases, 9 th and 10 th Revisions (ICD-9/10) codes.We identified new
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".