Nutrition and cancer – global and African perspectives: a focused update
Bibliographic record
Abstract
The burden of cancer worldwide is predicted to almost double by 2030 to nearly 23 million cases annually. The great majority of this increase is expected to occur in less economically developed countries, where access to expensive medical, surgical and radiotherapeutic interventions is likely to be limited to a small proportion of the population. This emphasises the need for preventive measures, as outlined in the declaration from the United Nations 2011 High Level Meeting on Non-communicable Diseases. The rise in incidence is proposed to follow from increasing numbers of people reaching middle and older ages, together with increasing urbanisation of the population with a nutritional transition from traditional diets to a more globalised 'Western' pattern, with a decrease in physical activity. This is also expected to effect a change in the pattern of cancers from a predominantly smoking and infection dominated one, to a smoking and obesity dominated one. The World Cancer Research Fund estimates that about a quarter to a third of the commonest cancers are attributable to excess body weight, physical inactivity and poor diet, making this the most common cause of cancers after smoking. These cancers are potentially preventable, but knowledge of the causes of cancer has not led to effective policies to prevent the export of a 'Western' pattern of cancers in lower income countries such as many in Africa.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".