Inequalities in survival and care across social determinants of health in a cohort of advanced lung cancer patients in Quebec (Canada): A high‐resolution population‐level analysis
Bibliographic record
Abstract
BACKGROUND: Advanced lung cancer patients exposed to breakthrough therapies like EGFR tyrosine kinase inhibitors (EGFR-TKI) may experience social inequalities in survival, partly from differences in care. This study examined survival by neighborhood-level socioeconomic and sociodemographic status, and geographical location of advanced lung cancer patients who received gefitinib, an EGFR-TKI, as first-line palliative treatment. Differences in the use and delay of EGFR-TKI treatment were also examined. METHODS: Lung cancer patients receiving gefitinib from 2001 to 2019 were identified from Quebec's health administrative databases. Accounting for age and sex, estimates were obtained for the median survival time from treatment to death, the probability of receiving osimertinib as a second EGFR-TKI, and the median time from biopsy to receiving first-line gefitinib. RESULTS: Among 457 patients who received first-line treatment with gefitinib, those living in the most materially deprived areas had the shortest median survival time (ratio, high vs. low deprivation: 0.69; 95% CI: 0.47-1.04). The probability of receiving osimertinib as a second EGFR-TKI was highest for patients from immigrant-dense areas (ratio, high vs. lowdensity: 1.95; 95% CI: 1.26-3.36) or from Montreal (ratio, other urban areas vs. Montreal: 0.39; 95% CI: 0.16-0.71). The median wait time for gefitinib was 1.27 times longer in regions with health centers peripheral to large centers in Quebec or Montreal in comparison to regions with university-affiliated centers (95% CI: 1.09-1.54; n = 353). CONCLUSION: This study shows that real-world variations in survival and treatment exist among advanced lung cancer patients in the era of breakthrough therapies and that future research on inequalities should also focus on this population.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".