Bibliographic record
Abstract
The Canadian Partnership Against Cancer (CPAC) was funded by the Government of Canada in 2009, to work with all stakeholders across the country to reduce cancer incidence, improve treatment, and improve the health of cancer survivors. As part of its initial mandate, CPAC has developed a detailed simulation model, the Cancer Risk Management Model (CRMM), designed to integrate and synthesize a wide range of empirical data, clinical trials and expertise to provide rigorous evidence for analysis and decision-making on cancer control policies. Three major cancer site models have been completed. However, the analysis and decision-making across these cites offer notable contrasts. Each cancer site model was based on cross-country consultation with a key focus centred around what were the main policy questions over the coming two to five years. Responses guided prioritization of various design elements in the models. For cervical, a high priority was how to organize HPV vaccination and Pap testing vs DNA tests. For colorectal a focus was comparative cost-effectiveness of FIT (at various thresholds) and FOBT screening. With a recent clinical trial showing a 20% reduction in mortality by using low dose CT (LDCT) screening for heavy smokers, the recent focus with the lung model includes annual versus biennial screening. Policy responses have been quite different. For cervical, the Ontario government has moved to vaccinate boys as well as girls. For colorectal screening, a pan-Canadian group of administrators of provincial screening programs is the key audience. For LDCT, audiences include both the federal preventive services task force and provincial cancer control agencies who are moving ahead with pilot projects. This paper contrasts the analyses for cervical, colorectal and lung cancer and their take-up in policy.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".