Validation of Canproj for projecting Canadian cancer incidence data
Bibliographic record
Abstract
INTRODUCTION: Cancer projections can provide key information to help prioritize cancer control strategies, allocate resources and evaluate current treatments and interventions. Canproj is a cancer-projection tool that builds on the Nordpred R-package by adding a selection of projection models. The objective of this project was to validate the Canproj R-package for the short-term projection of cancer rates. METHODS: We used national cancer incidence data from 1986 to 2014 from the National Cancer Incidence Reporting System and Canadian Cancer Registry. Cross-validation was used to estimate the accuracy of the projections generated by Canproj and relative bias (RB) was used as validation measure. The Canproj automatic model selection decision tree was also assessed. RESULTS: Five of the six models had mean RB between 5% and 10% and median RB around 5%. For some of the cancer sites that were more difficult to project, a shorter time period improved reliability. The Nordpred model was selected 79% of the time by Canproj automatic model selection although it had the smallest RB only 24% of the time. CONCLUSIONS: The Canproj package was able to provide projections that closely matched the real data for most cancer sites.
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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 | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".