Racial Disparities in Resection of Early Stage Non–Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Racial disparities in resection of non-small cell lung cancer (NSCLC) are well documented. Patient-level and system-level factors only partially explain these findings. Although physician-related factors have been suggested as mediators, empirical evidence for their contribution is limited. OBJECTIVE: To determine if racial disparities in receipt of thoracic surgery persisted after patients had a surgical consultation and whether there was a physician contribution to disparities in care. METHODS: The authors identified 19,624 patients with stage I-II NSCLC above 65 years of age from the Surveillance-Epidemiology and End-Results-Medicare database. They studied black and white patients evaluated by a surgeon within 6 months of diagnosis. They assessed for racial differences in resection rates among surgeons using hierarchical linear modeling. Our main outcome was receipt of NSCLC resection. A random intercept was included to test for variability in resection rates across surgeons. Interaction between patient race and the random surgeon intercept was used to evaluate for heterogeneity between surgeons in resection rates for black versus white patients. RESULTS: After surgical consultation, black patients were less likely to undergo resection (adjusted odds ratio, 0.57; 95% confidence interval, 0.47-0.69). Resection rates varied significantly between surgeons (P<0.001). A significant interaction between the surgeon intercept and race (P<0.05) showed variability beyond chance across surgeons in resection rates of black versus white patients. When the model included thoracic surgery specifalization the physician contribution to disparities in care was decreased. CONCLUSIONS: Racial disparities in resection of NSCLC exist even among patients who had access to a surgeon. Heterogeneity between surgeons in resection rates between black and white patients suggests a physician's contribution to observed racial disparities. Specialization in thoracic surgery attenuated this contribution.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".