Abstract PL05-03: Global trends in cancer: The imperative for prevention
Bibliographic record
Abstract
Abstract Despite the encouraging decrease in the incidence and death rates from all cancers combined in the United States, and the decrease in death rates from selected cancers in many industrialized countries, the number of people affected by cancer continues to increase rapidly worldwide. More than 12 million new cases and 7.5 million cancer deaths are estimated to have occurred globally in 2008. The number of deaths from cancer has increased by 25% since 1990 and is projected to nearly double (to 13.1 million) by 2030. Moreover, the global increase in the disease burden from cancer disproportionately affects low- and medium-resource countries, which currently account for nearly two-thirds of all cancer deaths, and are projected to contribute over 70% by 2030. Underlying this global increase in the cancer burden and its disproportionate impact on economically developing countries is a combination of both demographic changes and the shifting distribution of major risk factors. Three particularly important factors are the growth and aging of populations (especially longer life expectancy for massive numbers of young adults in developing countries), the entrenchment of modifiable risk factors (particularly cigarette smoking, Western diet, and physical inactivity), and the slower decline in cancers related to infectious etiologies in low-resource countries than in high-resource countries. Given these trends, the success of future efforts in global cancer control will require considerably greater emphasis on the development and implementation of effective population-level approaches to cancer prevention and early detection than exist currently in most countries. This talk will describe several successful models of tobacco control and interventions against chronic infections caused by hepatitis B and human papillomavirus that have been developed and implemented in high-risk developing countries. Other talks in the session will describe promising examples of population-level prevention in the U.S., Australia, and Canada. Citation Information: Cancer Prev Res 2010;3(12 Suppl):PL05-03.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".