Impact of Race on Outcomes of Advanced Stage Non-Small Cell Lung Cancer Patients Receiving Immunotherapy
Bibliographic record
Abstract
Background: The impact of race in advanced stage non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) is conflicting. Our study sought to examine racial disparities in time to treatment initiation (TTI), overall survival (OS), and progression-free survival (PFS) using a population that was almost equally black and white. Methods: This was a retrospective cohort study of stage IV NSCLC patients > 18 years receiving immunotherapy at our center between 2014 and 2021. Kaplan—Meier curves and the multivariate Cox proportional hazards model determined the predictors of OS and PFS. Analyses were undertaken using IBM PSAW (SPSS v.28). Results: Out of 194 patients who met the inclusion criteria, 42.3% were black (n = 82). In the multivariate analysis, there was no difference in PFS (HR: 0.96; 95% CI: 0.66,1.40; p = 0.846) or OS (HR: 0.99; 95% CI: 0.66, 1.48; p = 0.966). No difference in treatment selection was observed between white and black patients (p = 0.363), nor was there a difference observed in median time to overall treatment initiation (p = 0.201). Conclusions: No difference was observed in OS and PFS in black and white patients. Black patients’ reception of timelier immunotherapy was an unanticipated finding. Future studies are necessary to better understand how race impacts patient outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".