Racial disparities in the immunotherapeutic outcomes of patients with non-small cell lung cancer (NSCLC): An in-depth systematic review and meta-analysis.
Bibliographic record
Abstract
1579 Background: The utilization of immunotherapy has become prevalent in the therapeutic approach to non-small cell lung cancer (NSCLC), owing to its association with enhanced survival outcomes. Nevertheless, a notable gap exists in the available information regarding potential variations in the survival benefits of immunotherapy based on the racial demographics of NSCLC patients. Methods: A systematic search for articles published until January 2023 was performed on PubMed, EMBASE, and Google Scholar databases. Articles that aligned with the research objective were included, while non-English articles, case reports, conference abstracts, studies combining immunotherapy with other cancer therapies, and studies on small-cell lung cancer were excluded. Data required for review and analysis was independently abstracted into separate Excel files by two reviewers. Furthermore, Statistical analyses were performed using the Review Manager software, and the methodological quality evaluation was done using the Newcastle Ottawa Scale. Results: Seven cohort studies were used for review and analysis. A subgroup analysis of data from these studies showed that Black/African American and Asian NSCLC patients receiving immunotherapy had improved overall survival (OS) than White patients (HR: 0.84; 95% CI: 0.75 – 0.95; p = 0.006 and HR: 0.53; 95% CI: 0.30 – 0.93; p = 0.03, respectively). However, the difference in OS is statistically insignificant when Hispanic patients are compared with white patients (HR: 0.68; 95% CI: 0.46 – 1.00; p = 0.05). On the other hand, the subgroup analyses did not demonstrate any significant difference in progression-free survival (PFS) when comparing Black/African American, Asian or Hispanic patients to White patients (HR: 0.93; 95% CI: 0.79 – 1.09; p = 0.35, HR: 0.89; 95% CI: 0.51 – 1.55; p = 0.69, and HR: 1.01; 95% CI: 0.82 – 1.23; p = 0.96, respectively). Conclusions: Among non-small cell lung cancer (NSCLC) patients undergoing immunotherapeutic interventions, it is discerned that Black/African American and Asian individuals exhibit superior overall survival (OS) outcomes compared to their White counterparts. However, it is noteworthy that the observed racial disparity does not appear to exert a discernible influence on the progression-free survival (PFS) of NSCLC patients subjected to immunotherapy.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".