Dietary habits and prostate cancer detection: a case–control study
Bibliographic record
Abstract
BACKGROUND: Many studies have suggested that nutritional factors may affect prostate cancer development. The aim of our study was to evaluate the relationship between dietary habits and prostate cancer detection. METHODS: We studied 917 patients who planned to have transrectal ultrasonography-guided prostatic biopsy based on an elevated serum prostate-specific antigen (PSA) level, a rising serum PSA level or an abnormal digital rectal examination. Before receiving the results of their biopsy, all patients answered a self-administered food frequency questionnaire. In combination with pathology data we performed univariable and multivariable logistic regression analyses for the predictors of cancer and its aggressiveness. RESULTS: Prostate cancer was found in 42% (386/917) of patients. The mean patient age was 64.5 (standard deviation [SD] 8.3) years and the mean serum PSA level for prostate cancer and benign cases, respectively, was 13.4 (SD 28.2) mug/L and 7.3 (SD 4.9) mug/L. Multivariable analysis revealed that a meat diet (e.g., red meat, ham, sausages) was associated with an increased risk of prostate cancer (odds ratio [OR] 2.91, 95% confidence interval [CI] 1.55-4.87, p = 0.027) and a fish diet was associated with less prostate cancer (OR 0.54, 95% CI 0.32-0.89, p = 0.017). Aggressive tumours were defined by Gleason score (>/= 7), serum PSA level (>/= 10 mug/L) and the number of positive cancer cores (>/= 3). None of the tested dietary components were found to be associated with prostate cancer aggressivity. CONCLUSION: Fish diets appear to be associated with less risk of prostate cancer detection, and meat diets appear to be associated with a 3-fold increased risk of prostate cancer. These observations add to the growing body of evidence suggesting a relationship between diet and prostate cancer risk.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".