Population‐based cancer staging for oesophageal, gastric, and pancreatic cancer 2012‐2014: International Cancer Benchmarking Partnership <scp>SurvMark</scp>‐2
Bibliographic record
Abstract
Cancer stage at diagnosis is important information for management and treatment of individual patients as well as in epidemiological studies to evaluate effectiveness of health care system in managing cancer patients. Population-based studies to examine international disparities on cancer survival by stage, however, has been challenging due to the lack of international standardization on recording stage information and variation in stage completeness across regions and countries. The International Cancer Benchmarking Partnership (ICBP) previously assessed the availability and comparability of staging information for colorectal, lung, female breast and ovarian cancers. Stage conversion algorithms were developed to aggregate and map all stage information into a single staging system to allow international comparison by stage at diagnosis. In this article, we developed stage conversion algorithms for three additional cancers, namely oesophageal, gastric and pancreatic cancers. We examined all stage information available, evaluated stage completeness, applied each stage conversion algorithm, and assessed the magnitude of misclassification using data from six Canadian cancer registries (Alberta, Manitoba, Newfoundland, Nova Scotia, Prince Edward Island and Saskatchewan). In addition, we discussed five recommendations for registries to improve international cancer survival comparison by stage: (a) improve collection and completeness of staging data; (b) promote a comparable definition for stage at diagnosis; (c) promote the use of a common stage classification system; (d) record versions of staging classifications and (e) use multiple data sources for valid staging data.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".