Comparability of stage data in cancer registries in six countries: Lessons from the International Cancer Benchmarking Partnership
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it