The association between dietary acid load and odds of prostate cancer: a case-control study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Conflicting results exist regarding the associations between dietary acid load (DAL) and cancer risk. This study aimed to investigate the association between DAL and the odds of prostate cancer (PC) in the Iranian population. METHODS: One hundred and twenty participants (60 controls and 60 newly diagnosed PC patients) engaged in a hospital-based case-control study conducted from April to September 2015. A validated, 160-item semi-quantitative food frequency questionnaire (FFQ) was used to assess usual dietary intakes. DAL was calculated using potential renal acid load (PRAL) and net endogenous acid production (NEAP). Multivariate logistic regression was performed to estimate odds ratios (ORs). RESULTS: Both PRAL (OR = 5.44; 95% CI = 2.09-14.17) and NEAP (OR = 4.88; 95% CI = 2.22-13.41) were associated with increased odds of PC in the crude model. After adjusting for potential confounders (energy intake, smoking, physical activity, ethnicity, job, education, and medication use), being in the third category of PRAL (OR = 3.42; 95% CI = 1.11-8.65) and NEAP (OR = 3.88; 95% CI = 1.26-9.55) were significantly associated with increased odds of PC. CONCLUSION: Our findings suggest that dietary acid load may be linked to an increased risk of PC; however, further prospective studies with larger sample sizes and longer durations are necessary to validate these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".