A brief history of lung cancer in Canada: Care, contributions and challenges
Bibliographic record
Abstract
For over a century, lung cancer has been both the most common and the most lethal cancer in Canada, due to high populational tobacco exposure and other risk factors. Canada has significantly advanced the knowledge and treatment of lung cancer, as evidenced by important contributions to lung cancer screening, surgery, radiotherapy, systemic therapy, palliative and supportive care. There remain ongoing challenges to the provision of optimal lung cancer care in Canada, including: a gender gap in lung cancer rates and potential years of life lost, diagnostic and care inequity for Indigenous and other underrepresented populations, relatively low funding for lung cancer research, complex drug approval processes, restrictive funding structures for new treatments, poor access to palliative care and persistent stigma surrounding cigarette smoking and nicotine addiction. This paper highlights the significant Canadian contributions to the field of lung cancer, current challenges and future directions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".