The association between dairy products consumption and prostate cancer risk: a systematic review and meta-analysis
Bibliographic record
Abstract
In this study, we conducted a meta-analysis to estimate the relationship between the consumption of dairy products and the risk of prostate cancer. We searched PubMed, Embase and Cochrane databases for relevant articles and identified a total of thirty-three cohort studies between 1989 and 2020. The qualities of included studies were assessed using Newcastle-Ottawa scale. Pooled adjusted relative risks (RR) with 95 % CI were calculated. We performed subgroup analyses stratified by dairy type, prostate cancer type, follow-up years, treatment era, collection times, adjustment for confounders and geographic location. In the subgroup analysis stratified by prostate cancer type, the pooled RR were 0·98 (95 % CI 0·94, 1·03) in the advanced group, 1·10 (95 % CI 0·98, 1·24) in the non-advanced group and 0·92 (95 % CI 0·84, 1·00) in the fatal group. In the dose-response analysis, a positive association for the risk of prostate cancer was observed for total dairy products 400 g/d (RR: 1·02; 95 % CI 1·00, 1·03), total milk 200 g/d (RR: 1·02; 95 % CI 1·01, 1·03), cheese 40 g/d (RR: 1·01; 95 % CI 1·00, 1·03) and butter 50 g/d (RR: 1·03; 95 % CI 1·01, 1·05). A decreased risk was observed for the intake of whole milk 100 g/d (RR: 0·97; 95 % CI 0·96, 0·99). Our meta-analysis suggests that high intakes of dairy products may be associated with an increased risk of prostate cancer; however, since many of the studies were affected by prostate-specific antigen (PSA) screening bias, additional studies with an adjustment of PSA screening are needed.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.054 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".