Organization of population-based cancer control programs: Europe and the World
Bibliographic record
Abstract
As cancer is to a large extent avoidable and treatable, a cancer control program should be able to reduce mortality and morbidity and improve the quality of life of cancer patients and their families. However, the extent to which the goals of a cancer control program can be achieved will depend on the resource constraints a country faces. Such population-based cancer control plans should prioritize effective interventions and programs that are beneficial to the largest part of the population, and should include activities devoted to prevention, screening and early detection, treatment, palliation and end-of-life care, and rehabilitation. In order to develop a successful cancer control program, leadership and the relevant stakeholders, including patient organizations, need to be identified early on in the process so that all partners can take ownership and responsibility for the program. Various tools have been developed to aid them in the planning and implementation process. However, countries developing a national cancer control program would benefit from a discussion of different models for planning and delivery of population-based cancer control in settings with differing levels of resource commitment, in order to determine how best to proceed given their current level of commitment, political engagement and resources. As the priority assigned to different components of cancer control will differ depending on available resources and the burden and pattern of cancer, it is important to consider the relative roles of prevention, early detection, diagnosis, treatment, rehabilitation and palliative care in a cancer control program, as well as how to align available resources to meet prioritized needs. Experiences from countries with differing levels of resources are presented and serve to illustrate the difficulties in developing and implementing cancer control programs, as well as the innovative strategies that are being used to maximize available resources and enhance the quality of care provided to cancer patients around the world.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".