Translational Cancer Research: Balancing Prevention and Treatment to Combat Cancer Globally
Bibliographic record
Abstract
Cancer research is drawing on the human genome project to develop new molecular-targeted treatments. This is an exciting but insufficient response to the growing, global burden of cancer, particularly as the projected increase in new cases in the coming decades is increasingly falling on developing countries. The world is not able to treat its way out of the cancer problem. However, the mechanistic insights from basic science can be harnessed to better understand cancer causes and prevention, thus underpinning a complementary public health approach to cancer control. This manuscript focuses on how new knowledge about the molecular and cellular basis of cancer, and the associated high-throughput laboratory technologies for studying those pathways, can be applied to population-based epidemiological studies, particularly in the context of large prospective cohorts with associated biobanks to provide an evidence base for cancer prevention. This integrated approach should allow a more rapid and informed translation of the research into educational and policy interventions aimed at risk reduction across a population.
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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 | 0.053 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".