Racial Differences in Survival Among Advanced-stage Non–small-Cell Lung Cancer Patients Who Received Immunotherapy: An Analysis of the US National Cancer Database (NCDB)
Bibliographic record
Abstract
Lung cancer is the most common cause of cancer death among men and women in the United States, with significant racial disparities in survival. It is unclear whether these disparities persist upon equal utilization of immunotherapy. The purpose of this study was to evaluate the association between race and all-cause mortality among non-small-cell lung cancer (NSCLC) patients who received immunotherapy. We obtained data from the 2016 National Cancer Database on patients diagnosed with advanced-stage (III-IV) NSCLC from 2015 to 2016. Multivariable Cox proportional hazards models were used to calculate hazard ratios (HR) and 95% confidence intervals (95% CI) by race/ethnicity. A total of 2940 patients were included. Non-Hispanic (NH)-Black patients had a lower risk of death relative to NH-White patients (HR: 0.85; 95% CI: 0.73, 0.98) after adjusting for sociodemographic, clinical, and treatment factors. Formal tests of interaction evaluating race with Charlson-Deyo comorbidity score and race with area-level median income were nonsignificant. However, in stratified analyses, NH-Black versus NH-White patients had a lower risk of death in models adjusted for sociodemographic factors among those with at least 1 comorbidity (HR: 0.75; 95% CI: 0.57, 0.97), and those living in regions within the 2 lowest quartiles of median income (HR: 0.82; 95% CI: 0.68, 0.99). Among advanced-stage NSCLC patients who received immunotherapy, NH-Black patients experienced higher survival compared with NH-White patients. We urge the implementation of policies and interventions that seek to equalize access to care as a means of addressing differences in overall NSCLC survival by race.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".