The Under appreciated Role of Lifestyle and Nutrition in Cancer Prevention, Genesis, and Treatment
Bibliographic record
Abstract
This article presents a review of the impact of nutrition and lifestyle on the most frequently occurring cancers, including blood, bone, brain, breast, gastric, lung, oral, pancreatic and skin cancers. Heart disease and cancer are the leading causes of morbidity and mortality and the first and second leading causes of death in the United States. Risk of death declined more steeply for heart disease than cancer, offsetting the increase in heart disease deaths, which partially offsets the increase in cancer deaths resulting from demographic changes over the past four decades. Lung cancer is by far the most common cause of cancer-related mortality worldwide in many countries. The incidence rates of lung, colorectal and prostate cancers will continue to rise in the future decades due to the rise of ageing population. Pancreatic cancer is an aggressive malignancy with a poor long-term survival and there has been only slight improvement in outcomes over the past 30 years. Some of the most common contributing factors to various cancers include: genetics, tobacco use, infections, obesity, poor diet, physical inactivity, environmental pollution and hazards, ionizing and ultra-violet radiation (UVR), sunlight, cancer causing substances, chronic inflammation and immunosuppression. This article summarizes recent and tangible cancer control measures which include early detection, weight control, Mediterranean type diet, phytochemicals such as flavonoids, regular physical activity, therapeutic agents, chemotherapy, nano-medicine, medicinal plants and education through mass media awareness.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".