International differences in lung cancer survival by sex, histological type and stage at diagnosis: an ICBP SURVMARK-2 Study
Bibliographic record
Abstract
INTRODUCTION: Lung cancer has a poor prognosis that varies internationally when assessed by the two major histological subgroups (non-small cell (NSCLC) and small cell (SCLC)). METHOD: 236 114 NSCLC and 43 167 SCLC cases diagnosed during 2010-2014 in Australia, Canada, Denmark, Ireland, New Zealand, Norway and the UK were included in the analyses. One-year and 3-year age-standardised net survival (NS) was estimated by sex, histological type, stage and country. RESULTS: One-year and 3-year NS was consistently higher for Canada and Norway, and lower for the UK, New Zealand and Ireland, irrespective of stage at diagnosis. Three-year NS for NSCLC ranged from 19.7% for the UK to 27.1% for Canada for men and was consistently higher for women (25.3% in the UK; 35.0% in Canada) partly because men were diagnosed at more advanced stages. International differences in survival for NSCLC were largest for regional stage and smallest at the advanced stage. For SCLC, 3-year NS also showed a clear female advantage with the highest being for Canada (13.8% for women; 9.1% for men) and Norway (12.8% for women; 9.7% for men). CONCLUSION: Distribution of stage at diagnosis among lung cancer cases differed by sex, histological subtype and country, which may partly explain observed survival differences. Yet, survival differences were also observed within stages, suggesting that quality of treatment, healthcare system factors and prevalence of comorbid conditions may also influence survival. Other possible explanations include differences in data collection practice, as well as differences in histological verification, staging and coding across jurisdictions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".