Comparability of stage data in cancer registries in six countries: Lessons from the International Cancer Benchmarking Partnership
Bibliographic record
Abstract
The International Cancer Benchmarking Partnership is investigating cancer survival differences between six high-income nations using population-based cancer registry data. Differences in overall survival are often explained by differences in the stage at diagnosis and stage-specific survival. Comparing stage at diagnosis using cancer registry data is challenging because of different regional practices in defining stage, despite the existence of international staging classifications such as TNM. This paper describes how stage data may be reconciled for international analysis. Population-based cancer registry data were collected for 2.4 million adults diagnosed with colorectal, lung, breast (women) or ovarian cancer during 1995-2007 in Australia, Canada, Denmark, Norway, Sweden and the United Kingdom. The stage data received were coded to a variety of international systems, including the TNM classification, Dukes' for colorectal cancer, FIGO for ovarian cancer, and to national "localised, regional, distant" categorisations. To optimise comparability for analysis, a rigorous and repeatable process was defined to produce a final stage variable for each patient. An algorithm was also defined to map TNM, Dukes' and FIGO to a "localised, regional, distant" categorisation. We recommend how stage data should be recorded and processed to optimise comparability in population-based international comparisons of stage-specific cancer outcomes. The process we describe to produce comparable stage data forms a benchmark for future research. The algorithm to convert between TNM and a "localised, regional, distant" categorisation should be valuable for international studies, until global consensus is achieved to adhere to a single staging system like TNM.
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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 | 0.572 | 0.713 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".