The multidimensional role of cancer epidemiology in cancer prevention: discovery science and beyond
Bibliographic record
Abstract
The pivotal role of epidemiology in the identification of the causes of cancer is well recognized. However, after this identification, the translation of those findings into cancer prevention typically requires further epidemiological research. The role of cancer epidemiology in these next steps and in other aspects of cancer prevention is perhaps less well appreciated. Here we describe a framework for the multidimensional role of cancer epidemiology in cancer prevention including (1) hazard identification, (2) risk assessment, (3) understanding natural history, and (4) evaluating biological targets for prevention. The approaches required will vary depending on the type of prevention strategy. For example, primary prevention will usually require hazard identification and risk assessment and/or burden estimation, whereas secondary prevention will require studies of the natural history of disease. We describe the types of epidemiological study designs that are used to address these 4 dimensions and the role of novel methods in their success. We illustrate this with 5 examples: occupational radiation exposure, menopausal hormone therapy, per- and polyfluoroalkyl substances, obesity, and lung computed tomography screening. These examples show how the framework provides a systematic approach to define research questions and interpret results in the context of cancer prevention. This broader view of the field of cancer epidemiology also requires broader measures of success that go beyond the discovery of causes and estimates of population attributable fractions through to reductions of harmful exposures and eventually lowering cancer incidence and mortality in the affected populations.
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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 | 0.087 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| 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".