Disparities in outcomes and access to therapy options in hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) disproportionately impacts racial and ethnic minorities and patients with lower socioeconomic status. These social determinants of health (SDH) lead to disparities in access to care and outcomes. We aim to understand the relationship between SDH and survival and locoregional treatment options in HCC. METHODS: Using the National Cancer Database, we evaluated survival and access locoregional treatments including non-transplant surgery, liver transplant (LT), and liver-directed radiation therapy (LDRT) in patients with HCC diagnosed between 2004 and 2017. Variables including clinical stage, age, sex, race, income, rurality, year of diagnosis, facility type (FT), Charlson-Deyo score (CD), and insurance were evaluated. Cox proportional hazards multivariable regression and dominance analyses were used for analyses. RESULTS: In total, 140 340 patients were included. Worse survival was seen with advanced stage, older age, Black race, rurality, public insurance, treatment at a nonacademic center, and lower income. The top predictors for survival included stage, age, and income. Completion of non-transplant surgery was best predicted by stage, FT, and insurance type, whereas LT was predicted by age, year of diagnosis, and CD score. LDRT utilization was most associated with year of diagnosis, FT, and CD score. CONCLUSION: For patients with HCC, survival was predicted primarily by stage, age, and income. The primary sociodemographic factors associated with access to surgical treatments, in addition to FT, were insurance and income, highlighting the financial burdens of health care. Work is needed to address disparities in access to care, including improved insurance access, addressing financial inequities and financial toxicities of treatments, and equalizing care opportunities in community centers.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".