The International Cancer Benchmarking Partnership: An international collaboration to inform cancer policy in Australia, Canada, Denmark, Norway, Sweden and the United Kingdom
Bibliographic record
Abstract
The International Cancer Benchmarking Partnership (ICBP) was initiated by the Department of Health in England to study international variation in cancer survival, and to inform policy to improve cancer survival. It is a research collaboration between twelve jurisdictions in six countries: Australia (New South Wales, Victoria), Canada (Alberta, British Columbia, Manitoba, Ontario), Denmark, Norway, Sweden, and the United Kingdom (England, Northern Ireland, Wales). Leadership is provided by policymakers, with academics, clinicians and cancer registries forming an international network to conduct the research. The project currently has five modules examining: (1) cancer survival, (2) population awareness and beliefs about cancer, (3) attitudes, behaviours and systems in primary care, (4) delays in diagnosis and treatment, and their causes, and (5) treatment, co-morbidities and other factors. These modules employ a range of methodologies including epidemiological and statistical analyses, surveys and clinical record audit. The first publications have already been used to inform and develop cancer policies in participating countries, and a further series of publications is under way. The module design, governance structure, funding arrangements and management approach to the partnership provide a case study in conducting international comparisons of health systems that are both academically and clinically robust and of immediate relevance to policymakers.
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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 | 0.186 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".