Chronic kidney diseases and the risk of colorectal cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective We conducted this review to offer a comprehensive search and up-to-date overview of the currently available information about the probability risk of colorectal cancer among chronic kidney disease patients.Method We performed a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews (PRISMA) and meta-analysis guidelines. We identified, reviewed, and extracted from Scopus, PubMed, EMBASE, and Komaki Databases for research publications on chronic kidney disease and colorectal cancer published between February 2016 and January 2023. We meta-analyzed the prevalence of colorectal cancer with chronic kidney disease. We ran a random effect meta-regression. Risk-of-bias assessment was evaluated using the Newcastle-Ottawa Scale. The systematic review was registered with PROSPERO (CRD42023400983).Results The risk of CRC in chronic kidney diseases was reported in 50 research studies, which included 4,337,966 people from 16 different countries. SIR of CRC was obtained from 14 studies and showed a significant relationship between CRC with CKD patients, with a pooled SIR of 1.33; 95% CI (1.30–1.36), with higher heterogeneity (Q = 121.82, P < 0.001, and I2 = 86.9%). Metaregression showed that there was no significant correlation between the risk of CRC and the proportion of males or age.Conclusion Overall, this study shows that patients with chronic kidney disease have a significantly increased risk of colorectal cancer. More studies with larger sample sizes, and robust surveillance 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 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| 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 | 0.001 |
| 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".