Patterns of venous thromboembolism risk, treatment, and outcomes among patients with cancer from uninsured and vulnerable populations
Bibliographic record
Abstract
The epidemiology of cancer-associated thrombosis (CAT) among uninsured and vulnerable populations in the US is not well-characterized. We performed a retrospective cohort study for patients with newly diagnosed cancer from 2011 to 2020 at Harris Health System, which cares for uninsured residents in the Houston metropolitan area. Patient demographics, NCI comorbidity index, area of deprivation index (ADI), cancer histology, staging, and systemic therapy data were extracted. CAT included overall venous thromboembolism (VTE) or pulmonary embolism +/- lower extremity deep vein thrombosis (PE/LE-DVT) within 1 year of diagnosis. We used multivariable Fine-Gray models to assess the associations with CAT accounting for death as a competing risk. Among 15 342 patients, 74% were uninsured and 84% lived in socioeconomically disadvantaged neighborhoods. There were 16% Non-Hispanic White (NHW), 28% Non-Hispanic Black (NHB), 50% Hispanic (27% Mexican), and 6% Asian/Pacific Islanders (API). The 1-year CAT incidence rate was 14.6%. Overall VTE was lower for Hispanics versus NHW (SHR 0.87 [0.76-0.99]) and API versus NHW (SHR 0.58 [0.44-0.77]). PE/LE-DVT was higher for NHB versus NHW (SHR 1.18 [1.01-1.39]). CAT was also associated with chemotherapy-based regimens (+/- immunotherapy), age, obesity, cancer type/staging, VTE history, and recent hospitalization. NCI comorbidity and ADI scores were associated with mortality but not CAT. In a large cohort of underserved patients with cancer, we identified an elevated incidence of CAT with known and novel risk predictors. Hispanics had lower adjusted rates of CAT and mortality. Our findings highlight the need to investigate and incorporate vulnerable populations in clinical trials.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".