Achievements and future of nutritional cancer epidemiology
Bibliographic record
Abstract
We consider some of the earlier work and some recent results on diet and cancer (since the 2007 WCRF/AICR report on Diet and Cancer), discuss challenges facing nutritional cancer epidemiology, and consider the field from the perspective of the need to apply what we know in cancer control. We highlight 2 current difficulties; first, we are uncertain on the stage of carcinogenesis on which many nutritional factors act, second, we often do not know what dose of a nutritional factor is needed to achieve its expected protective effect in humans. Part of the difficulty is the measurement error associated with food frequency questionnaires. Calibration studies (as in the European Prospective Investigation on diet and Cancer) have helped to reduce this, and pooled studies have helped to clarify associations. However, there is too little work on new biomarkers of nutrition; with the new techniques available (especially proteomics, and metabolomics) it should be possible to identify more and better biomarkers that could be used in repeated blood or urine samples and give very good information on diet. In cancer control we need to determine how to reduce the prevalence of obesity and increase physical activity in populations, not whether they are causal factors. This could be achieved by community-based interventions linked to some of the new cohort studies being initiated. We conclude we have reached the stage in nutritional cancer epidemiology where we need to concentrate more on applying the lessons we have learnt, than in seeking new aetiological associations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".