Concurrent chronic kidney disease in patients with inflammatory bowel disease, a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Inflammatory bowel disease (IBD) is a multi-organ autoimmune disease that commonly affects the gastrointestinal tract, but can also affect other organs throughout the body. Less is known, however, about kidney involvement in IBD. Although IBD has been associated with chronic kidney disease (CKD) and end-stage renal disease (ESRD), these results have been inconsistent. The present study analyzed the prevalence of concurrent CKD and ESRD in patients with IBD. Methods PubMed, Cochrane, Web of Science, and Embase were searched for studies published through October 2023 on IBD patients with concurrent CKD or ESRD. Outcomes included the incidence rates and odds ratios (OR) of concurrent CKD and ESRD in IBD patients. The quality of included studies was assessed using the Newcastle-Ottawa Scale, and sequential sensitivity was analyzed. Publication bias was evaluated using Egger’s test. Results Nine studies were included in this meta-analysis. The combined results of eight studies, which included 239,042 IBD patients, showed that the prevalence of CKD in IBD patients was 5% (95% confidence interval [CI]: 1–9%). The combined results of two studies, which included 40,341 IBD patients, showed that the prevalence of ESRD in IBD patients was 0.2% (95% CI: −0.08–0.12%). The combined results of six case–control studies reported that the risk of CKD was significantly higher in patients with than without IBD (OR 1.36, 95% CI: 1.08–1.70, p = 0.008). Conclusion Although studies have shown an increased risk of CKD in IBD, due to the small number of included studies and high heterogeneity across studies, it is not enough to definitively conclude that CKD is more common in patients with IBD. But patients with IBD should be regularly monitored for CKD. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/ .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".