Thromboembolic risk in lung cancer patients receiving systemic therapy
Bibliographic record
Abstract
Abstract In this retrospective study, we investigated the influence of chemotherapy and immunotherapy on thromboembolic risk among US Veterans with lung cancer during their first six months of systemic therapy. Patients in the study cohort received treatment with common frontline agents that were divided into four groups: chemotherapy alone, immunotherapy alone, combination of chemo- and immunotherapies, and molecularly targeted therapies. The latter served as a control group of systemically treated lung cancer patients who received neither chemotherapy nor immunotherapy. The cohort experienced a 6.8% overall incidence of thromboembolic events with a median time to event of 49 days, but the analysis demonstrated significantly different rates among the different treatment groups. We explored models incorporating multiple confounding variables as well as the competing risk of death, and these results indicated that both chemotherapy and immunotherapy were associated with an increased incidence of thrombosis, either when given alone or combined, compared with the control group (6.91%, 9.09%, and 7.47% respectively versus 3.68%, p < 0.024). Both the Khorana score assessing thrombosis risk for cancer patients and the Charlson comorbidity score were found to be associated with increased risk of thrombosis in our analyses. Paradoxically, we found an association between risk of thrombosis and the use of prophylactic anticoagulation or aspirin during the first month of systemic treatment, accounting for several confounding variables including a patient’s prior history of thrombosis. Additionally, our data suggest that thromboembolic events may occur more commonly in lung cancer patients treated with immunotherapy compared with chemotherapy. Further study is warranted to better determine the drivers of thromboembolic risk and to identify ways to mitigate this risk for 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".