Lung cancer stage at diagnosis and immigrant English/French language proficiency: a retrospective population level cohort study of urban residents in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Lung and bronchial cancer is the most diagnosed cancer among both sexes in Canada and has one of the lowest survival rates. METHODS: This population level retrospective cohort study examined the associations between the lung cancer stage at diagnosis and English/French fluency. The study used multiple linked health-administrative databases to create a cohort of urban-dwelling Ontarian immigrants and long-term residents aged 45-105 diagnosed with incident lung cancer between 1 January 2010 and 31 December 2020. Modified Poisson regression was used to examine the risk of diagnosis at early vs. late stages among immigrants who do not speak English or French fluently compared with immigrants who speak English or French fluently. The fully adjusted model included age, sex, neighborhood-area income quintile, lung cancer type, number of primary care visits prior to diagnosis, and region of origin. RESULTS: Approximately 57.7% of the 96,613 people diagnosed with incident lung cancer between 2010 and 2020 were diagnosed at the late stage. Non-English/French fluent immigrants were no more likely to be diagnosed at a late stage than English/French fluent immigrants and long-term residents (57.6% vs. 57.8% and 57.7%). However, in fully adjusted models, people living in lower neighborhood income quintiles were more likely to be diagnosed at a late stage (e.g., income quintile 1 [lowest] vs. quintile 5 [highest]: [ARR 1.08; 95% CI: 1.02-1.15]), as were immigrants from the Caribbean [ARR 1.16; 95% CI: 1.05-1.29] and South Asia [ARR 1.10; 95% CI: 1.02-1.19]. CONCLUSIONS: Although lung cancer is frequently diagnosed at a late stage in Ontario and we found socioeconomic inequalities, fluency in Canada's official languages was not associated with late diagnosis in this study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".