Risk of Prostate Cancer in Chronic Kidney Disease Patient: A Meta-Analysis using Observational Studies
Bibliographic record
Abstract
Background: Both clinical and experimental findings demonstrated a rise in prostate cancer in chronic renal illness. However, the clinical data associated with CKD was not looked at the context of prostate cancer. The study aims to investigate prostate cancer risk in CKD patients using clinical data via systemic review and meta-analysis. Materials and Methods: Using pertinent pairing keywords, I carried out a thorough exploration of PubMed/MEDLINE and Web of Science. The pooled HR with 95% CI of the considered clinical findings was estimated involving the general inverse variance outcome type. With RevMan 5.3, the total pooled estimate meta-analysis was evaluated utilizing the random effects model. Results: Total of six findings were considered for this analysis, with a total of 2,430,246 participants. The age and mean follow-up of the included patients and studies ranged from 55 to 67.4 years and 10.1 to 12 years, respectively. The meta-analysis showed no significant risk of prostate cancer among CKD patients (HR: 0.92; 95% CI: 0.60-1.41 ; P = 0.70). The results from subgroup analysis based on eGFR levels ranged ≥30-59 ml/min per 1.73 m 2 and also found no significant risk of prostate cancer among CKD patients (HR: 1.04; 95% CI: 0.92-1.18; P = 0.52). Here I did not report statistical heterogeneity found (Q = 0.56, I 2 = 0%, P = 0.87). As per the Newcastle-Ottawa scale, the included studies suggested good quality. Conclusion: The results suggest no significant risk of developing prostate cancer among CKD patients. Therefore, well-designed prospective cohort studies with stages of CKD and clear predefined prior history and causative factors are needed to support the present evidence strongly.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.067 |
| Bibliometrics | 0.007 | 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 | 0.003 |
| 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".