Is England closing the international gap in cancer survival?
Bibliographic record
Abstract
BACKGROUND: We provide an up-to-date international comparison of cancer survival, assessing whether England is 'closing the gap' compared with other high-income countries. METHODS: Net survival was estimated using national, population-based, cancer registrations for 1.9 million patients diagnosed with a cancer of the stomach, colon, rectum, lung, breast (women) or ovary in England during 1995-2012. Trends during 1995-2009 were compared with estimates for Australia, Canada, Denmark, Norway and Sweden. Clinicians were interviewed to help interpret trends. RESULTS: Survival from all cancers remained lower in England than in Australia, Canada, Norway and Sweden by 2005-2009. For some cancers, survival improved more in England than in other countries between 1995-1999 and 2005-2009; for example, 1-year survival from stomach, rectal, lung, breast and ovarian cancers improved more than in Australia and Canada. There has been acceleration in lung cancer survival improvement in England recently, with average annual improvement in 1-year survival rising to 2% during 2010-2012. Survival improved more in Denmark than in England for rectal and lung cancers between 1995-1999 and 2005-2009. CONCLUSIONS: Survival has increased in England since the mid-1990s in the context of strategic reform in cancer control, however, survival remains lower than in comparable developed countries and continued investment is needed to close the international survival gap.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".