The impact of body mass index on prostate cancer: An updated systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Increasing evidence suggested obesity was associated with the risk of prostate cancer. Also, the association between prostate cancer risk and obesity has received much attention in recent years, but the results are still unclear. Therefore, the current systematic review and meta-analysis aimed to evaluate the impact of body mass index (BMI) on prostate cancer. METHODS: We systematically searched PubMed, Google Scholar, Scopus and Cochrane databases with the appropriate key terms to identify the eligible articles related to the impact of BMI on prostate cancer. The Newcastle-Ottawa checklist was used for the quality assessment of studies, and the meta-analysis was carried out using Review Manager 5.3. RESULTS: The present review includes 23 studies that fulfilled the criteria for inclusion. In the meta-analysis, a significant difference was observed between the obese and normal weight (P < .001) and 54% of obese has a risk compared to normal weight. Heterogeneity between the fifteen studies was high (I2 = 100%). Test for overall effect: Z = 8.77 (P < .001) (odds ratio [OR] = 0.32 confidence interval [CI]: 0.25-0.42). However, there was no significant difference observed between the overweight and normal weight (P = .75). Heterogeneity between the fifteen studies is high (I2 = 100%). CONCLUSION: Prostate cancer is a common malignancy that poses a threat to the health of men. Obesity is associated with a higher risk of death from prostate cancer based on the findings of the included studies. Furthermore, wherever possible, the impact of weight change on prostate cancer patient mortality should be investigated.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.011 | 0.011 |
| 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) | 0.004 | 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".