Variation in the Association between Antineoplastic Therapies and Venous Thromboembolism in Patients with Active Cancer
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) is a major cause of death in cancer patients. Although patients with cancer have numerous risk factors for VTE, the relative contribution of cancer treatments is unclear. OBJECTIVE: The objective of this study is to evaluate the association between cancer therapies and the risk of VTE. METHODS: From UK Clinical Practice Research Datalink, data on patients with first cancer diagnosis between 2008 and 2016 were extracted along with information on hospitalization, treatments, and cause of death. Primary outcome was active cancer-associated VTE. To establish the independent effects of risk factors, adjusted subhazard ratios (adj-SHR) were calculated using Fine and Gray regression analysis accounting for death as competing risk. RESULTS: Among 67,801 patients with a first cancer diagnosis, active cancer-associated VTE occurred in 1,473 (2.2%). During a median observation time of 1.2 years, chemotherapy, surgery, hormonal therapy, radiation therapy, and immunotherapy were given to 71.1, 37.2, 17.2, 17.5, and 1.4% of patients with VTE, respectively. The active cancers associated with the highest risk of VTE-as assessed by incidence rates-included pancreatic cancer, brain cancer, and metastatic cancer. Chemotherapy was associated with an increased risk of VTE (adj-SHR: 3.17, 95% confidence interval [CI]: 2.76-3.65) while immunotherapy with a not significant reduced risk (adj-SHR: 0.67, 95% CI: 0.30-1.52). There was no association between VTE and radiation therapy (adj-SHR: 0.91, 95% CI: 0.65-1.27) and hormonal therapies. CONCLUSION: VTE risk varies with cancer type. Chemotherapy was associated with an increased VTE risk, whereas with radiation and immunotherapy therapy, an association was not confirmed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".