Meta‐Analysis: Prevalence of Eating Disorders in Inflammatory Bowel Disease
Bibliographic record
Abstract
ABSTRACT Background and Aims Disentangling whether disordered eating is a cause, consequence or manifestation of inflammatory bowel disease (IBD) symptoms remains a challenge. We conducted an updated systematic review and the first meta‐analysis to estimate the prevalence of eating disorders in individuals with IBD. Methods We systematically searched MEDLINE, Embase and PsycINFO from inception to 28 October 2025, for original observational studies reporting the prevalence of at least one eating disorder in an IBD population. Pooled prevalence estimates were calculated using random‐effects models and stratified by IBD type, sex, age and assessment method. Between‐study heterogeneity was assessed using Q and I 2 statistics. Results Twenty‐three studies were included. Prevalence estimates varied substantially depending on how eating disorders were assessed. Studies using self‐report questionnaires yielded a pooled prevalence of 13.60% (95% CI = 9.86%–17.81%; I 2 = 90.7%; n = 18), whereas studies employing physician‐assigned diagnoses yielded a lower pooled prevalence of 2.84% (95% CI = 0.00%–9.03%; I 2 = 99.9%; n = 5). The highest prevalence was observed in studies using the Nine‐Item Avoidant/Restrictive Food Intake Disorder Screen, with a pooled estimate of 17.10% (95% CI = 12.81%–21.88%; I 2 = 87.4%; n = 9). No significant differences in prevalence were found by sex, IBD subtype, age at time of study or disease activity. Conclusions Eating disorders are prevalent among individuals with IBD, particularly avoidant/restrictive types. These findings highlight the need for improved screening and greater clinical awareness to better detect and manage disordered eating in the IBD population.
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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.061 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".