Infections in primary sclerosing cholangitis and inflammatory bowel disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose Primary sclerosing cholangitis (PSC) is a cholestatic liver disease that frequently coexists with inflammatory bowel disease (IBD). The risk of infections in patients with concurrent PSC-IBD remains unclear. The aim of this study was to identify the event rate of infections and associated risk factors in PSC-IBD patients. Methods MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were searched from inception to September 12, 2024 for studies examining event rate or risk factors for infection in patients with PSC-IBD. The primary outcome was the event rate of all-cause and site-specific infections as well as infection-related mortality. The secondary outcome was risk factors for infection. Random-effects models were used to calculate pooled odds ratios (OR) with 95% confidence intervals (CI) comparing the event rate of all-cause infections in PSC-IBD patients to those with just PSC and just IBD. I2 values more than 50% suggested substantial heterogeneity. Results Eighty-one studies were included. The pooled event rate of all-cause infections in patients with PSC-IBD was 25.1% (95% CI, 17.0%-33.2%, I2 = 99.2%). PSC-IBD patients had significantly increased odds of all-cause infection (OR 3.67, 95% CI, 2.07-6.52, I2 = 41.9%), sepsis (OR 3.35, 95% CI, 2.29-4.91, I2 = 9.1%), and infection-related mortality (OR 11.25, 95% CI, 2.03-62.37, I2 = 0) compared to those with IBD but not those with PSC. Conclusion Patients with PSC-IBD appear to be at increased risk of all-cause infection, sepsis, and mortality compared to those with IBD alone.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".