Research Strategies for Nutritional and Physical Activity Epidemiology and Cancer Prevention
Bibliographic record
Abstract
Abstract Very large international and ethnic differences in cancer rates exist, are minimally explained by genetic factors, and show the huge potential for cancer prevention. A substantial portion of the differences in cancer rates can be explained by modifiable factors, and many important relationships have been documented between diet, physical activity, and obesity, and incidence of important cancers. Other related factors, such as the microbiome and the metabolome, are emerging as important intermediary components in cancer prevention. It is possible with the incorporation of newer technologies and studies including long follow-up and evaluation of effects across the life cycle, additional convincing results will be produced. However, several challenges exist for cancer researchers; for example, measurement of diet and physical activity, and lack of standardization of samples for microbiome collection, and validation of metabolomic studies. The United States National Cancer Institute convened the Research Strategies for Nutritional and Physical Activity Epidemiology and Cancer Prevention Workshop on June 28–29, 2016, in Rockville, Maryland, during which the experts addressed the state of the science and areas of emphasis. This current paper reflects the state of the science and priorities for future research. Cancer Epidemiol Biomarkers Prev; 27(3); 233–44. ©2017 AACR.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".