Socioeconomic disparity trends in diagnostic imaging, treatments, and survival for non‐small cell lung cancer 2007‐2016
Bibliographic record
Abstract
Socioeconomic status (SES) has led to treatment and survival disparities; however, limited data exist for non-small cell lung cancer (NSCLC). This study investigates the impact of SES on NSCLC diagnostic imaging, treatment, and overall survival (OS), and describes temporal disparity trends. The Ontario Cancer Registry was used to identify NSCLC patients diagnosed between 2007 and 2016. Through linkage to administrative datasets, patients' demographics, imaging, treatment, and survival were obtained. Based on median household neighborhood income, the Ontario population was divided into five income quintiles (Q1-Q5; Q1 = lowest income). Multivariable regressions assessed SES association with OS, imaging, treatment receipt, and treatment delay, and their interaction with year of diagnosis to understand temporal trends. Endpoints were adjusted for demographics, stage and comorbidities, along with treatments and imaging for OS. A total of 50 542 patients were identified. Higher SES patients (Q5 vs. Q1) showed improved 5-year OS (hazard ratio, 0.89; 95% confidence interval [CI], 0.87-0.92; P < .0001) and underwent greater magnetic resonance imaging head (stages IA-IV; odds ratio [OR], 1.24; 95% CI, 1.16-1.32; P < .0001), lung resection (IA-IIIA; OR, 1.58; 95% CI, 1.43-1.74; P < .0001), platinum-based vinorelbine adjuvant chemotherapy (IB-IIIA; OR, 1.63; 95% CI, 1.39-1.92; P < .0001), palliative radiation (IV; OR, 1.14; 95% CI, 1.05-1.25; P = .023), and intravenous chemotherapy (IV; OR, 1.45; 95% CI, 1.32-1.60; P < .0001). Lower SES patients underwent greater thoracic radiation (IA-IIIB; OR, 0.86; 95% CI, 0.79-0.94; P = .0003). Across 2007-2016, socioeconomic disparities remain largely unchanged (interaction P > .05) despite widening income inequality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".