International trends in oesophageal cancer survival by histological subtype between 1995 and 2014
Bibliographic record
Abstract
INTRODUCTION: Survival from oesophageal cancer remains poor, even across high-income countries. Ongoing changes in the epidemiology of the disease highlight the need for survival assessments by its two main histological subtypes, adenocarcinoma (AC) and squamous cell carcinoma (SCC). METHODS: December 2015, from cancer registries covering seven participating countries with similar access to healthcare (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the UK). 1-year and 3-year age-standardised net survival alongside incidence rates were calculated by country, subtype, sex, age group and period of diagnosis. RESULTS: 111 894 cases of AC and 73 408 cases of SCC were included in the analysis. Marked improvements in survival were observed over the 20-year period in each country, particularly for AC, younger age groups and 1 year after diagnosis. Survival was consistently higher for both subtypes in Australia and Ireland followed by Norway, Denmark, New Zealand, the UK and Canada. During 2010 to 2014, survival was higher for AC compared with SCC, with 1-year survival ranging from 46.9% (Canada) to 54.4% (Ireland) for AC and 39.6% (Denmark) to 53.1% (Australia) for SCC. CONCLUSION: Marked improvements in both oesophageal AC and SCC survival suggest advances in treatment. Less marked improvements 3 years after diagnosis, among older age groups and patients with SCC, highlight the need for further advances in early detection and treatment of oesophageal cancer alongside primary prevention to reduce the overall burden from the disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".