Dietary Fat and Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Data from histopathological and migratory studies suggest that 1 or more late stage environmental promoters are involved in the development of clinical carcinoma of the prostate. Laboratory investigations and variously designed epidemiological studies in man have suggested that dietary fat may be one of these candidate tumor promoters but other studies have questioned this association. The biologically plausible associations that have been hypothesized include total energy consumption, altered androgen metabolism, oxidative stress, specific fatty acid consumption and pesticide intake. We provide a critical appraisal of the existing evidence for an association between dietary fat consumption and prostate cancer, and review the biologically plausible relationships. MATERIALS AND METHODS: All 33 published case-control and cohort studies that examined the relationship between prostate cancer and dietary fat or specific fatty food types were critically appraised. Eight studies suggested a statistically significant association, and many studies noted significant associations for specific types of fatty foods (eg milk or meat) and prostate cancer. RESULTS: In light of the inherent biases in the methodology of studying dietary fat intake and carcinoma of the prostate, we conclude that the evidence is consistent. CONCLUSIONS: Corroborative studies in humans are required to better define this relationship. Prospective studies of dietary intervention should be encouraged.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".