Lung cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK: a population-based study, 2004–2007
Bibliographic record
Abstract
BACKGROUND: The authors consider whether differences in stage at diagnosis could explain the variation in lung cancer survival between six developed countries in 2004-2007. METHODS: Routinely collected population-based data were obtained on all adults (15-99 years) diagnosed with lung cancer in 2004-2007 and registered in regional and national cancer registries in Australia, Canada, Denmark, Norway, Sweden and the UK. Stage data for 57 352 patients were consolidated from various classification systems. Flexible parametric hazard models on the log cumulative scale were used to estimate net survival at 1 year and the excess hazard up to 18 months after diagnosis. RESULTS: Age-standardised 1-year net survival from non-small cell lung cancer ranged from 30% (UK) to 46% (Sweden). Patients in the UK and Denmark had lower survival than elsewhere, partly because of a more adverse stage distribution. However, there were also wide international differences in stage-specific survival. Net survival from TNM stage I non-small cell lung cancer was 16% lower in the UK than in Sweden, and for TNM stage IV disease survival was 10% lower. Similar patterns were found for small cell lung cancer. CONCLUSIONS: There are comparability issues when using population-based data but, even given these constraints, this study shows that, while differences in stage at diagnosis explain some of the international variation in overall lung cancer survival, wide disparities in stage-specific survival exist, suggesting that other factors are also important such as differences in treatment. Stage should be included in international cancer survival studies and the comparability of population-based data should be improved.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".