P1284 Prevalence of immune-mediated extraintestinal manifestations in paediatric Inflammatory Bowel Disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Concurrent immune-mediated extraintestinal manifestations (IM-EIMs; Table 1) are common in paediatric-onset inflammatory bowel disease (pIBD), but the overall epidemiology is unknown. This systematic review and meta-analysis aimed to summarise the prevalence and management of EIMs in pIBD. Methods MEDLINE, EMBASE and CENTRAL, clinicaltrials.gov and conference abstracts were searched up to May 6, 2024, for studies that included pIBD (2-17 years old) with secondarily diagnosed EIMs. The primary outcome included the prevalence of IM-EIMs among pIBD. Secondary outcomes included efficacy and adverse effects of treatment on EIMs. Meta-analysis included pooled proportions with DerSimonian-Laird random-effects analysis. Results The pooled proportion of IM-EIMs among pIBD was 8.4% (95% CI, 6.5-11.0%; I2=97%) among 53 eligible studies. The pooled proportion of IM-EIMs was 10.3% (95% CI, 7.3-13.8%; I2=95%) among 37 Crohn’s disease (CD) studies, compared to 11.3% (95% CI, 7.8-15.3%; I2=93%) among 39 ulcerative colitis (UC) studies. Enthesitis was the highest reported individual IM-EIM (23%, 95% CI, 4-50%; I2=94.7%) in pIBD. Arthritis was the second highest reported individual IM-EIM (8%, 95% CI, 6-10%; I2=96%) in pIBD, followed by primary sclerosing cholangitis (PSC) in UC (5%, 95% CI, 4-7%; I2=81%; Table 2). Uveitis, pyoderma gangrenosum, autoimmune hepatitis, and PSC in CD were the lowest reported IM-EIMs at 1% or less. The largest difference in reported proportions between IBD phenotypes was PSC, which was 4% higher in UC than CD. Only one eligible study described treatment response. Conclusion This meta-analysis identified that while musculoskeletal manifestations are relatively common, fewer than 5% of pIBD patients experience ophthalmological, dermatological, and hepatic IM-EIMs. There was significant methodological and statistical heterogeneity as expected for the relatively small number of individual paediatric studies and likely resulted in the large difference of pooled proportions between individual IM-EIMs. Multicentre collaborative efforts are needed to systematically describe the epidemiology and management outcomes of EIMs in pIBD in the current era of advanced therapies.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| 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) | 0.006 | 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".