Breast cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK, 2000-2007: a population-based study
Bibliographic record
Abstract
BACKGROUND: We investigate whether differences in breast cancer survival in six high-income countries can be explained by differences in stage at diagnosis using routine data from population-based cancer registries. METHODS: We analysed the data on 257,362 women diagnosed with breast cancer during 2000-7 and registered in 13 population-based cancer registries in Australia, Canada, Denmark, Norway, Sweden and the UK. Flexible parametric hazard models were used to estimate net survival and the excess hazard of dying from breast cancer up to 3 years after diagnosis. RESULTS: Age-standardised 3-year net survival was 87-89% in the UK and Denmark, and 91-94% in the other four countries. Stage at diagnosis was relatively advanced in Denmark: only 30% of women had Tumour, Nodes, Metastasis (TNM) stage I disease, compared with 42-45% elsewhere. Women in the UK had low survival for TNM stage III-IV disease compared with other countries. CONCLUSION: International differences in breast cancer survival are partly explained by differences in stage at diagnosis, and partly by differences in stage-specific survival. Low overall survival arises if the stage distribution is adverse (e.g. Denmark) but stage-specific survival is normal; or if the stage distribution is typical but stage-specific survival is low (e.g. UK). International differences in staging diagnostics and stage-specific cancer therapies should be investigated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".