The Role of Dietary Fat throughout the Prostate Cancer Trajectory
Bibliographic record
Abstract
Prostate cancer is the second most common cancer diagnosed world-wide; however, patients demonstrate exceptionally high survival rates. Many lifestyle factors, including obesity and diet, are considered risk factors for advanced prostate cancer. Dietary fat is a fundamental contributor to obesity and may be specifically important for prostate cancer patients. Prostate cancer treatment can result in changes in body composition, affecting quality of life for survivors by increasing the risk of co-morbidities, like cardiovascular disease and diabetes. We aim to examine dietary fat throughout the prostate cancer treatment trajectory, including risk, cancer development and survivorship. Focusing on one specific nutrient throughout the prostate cancer trajectory provides a unique perspective of dietary fat in prostate cancer and the mechanisms that may exacerbate prostate cancer risk, progression and recurrence. Through this approach, we noted that high intake of dietary fat, especially, high intake of animal and saturated fats, may be associated with increased prostate cancer risk. In contrast, a low-fat diet, specifically low in saturated fat, may be beneficial for prostate cancer survivors by reducing tumor angiogenesis and cancer recurrence. The insulin-like growth factor (IGF)/Akt signaling pathway appears to be the key pathway moderating dietary fat intake and prostate cancer development and progression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".