New Onset Inflammatory Bowel Disease Risk Following Bariatric Surgery: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Background: While bariatric surgery may reduce obesity-associated inflammation, alterations in gut microbiome and nutrition could impact inflammatory bowel disease (IBD) risk. This study aimed to investigate the association between bariatric surgery and new onset IBD. Methods: A systematic review and meta-analysis of observational studies was conducted from inception to 31 January 2024. Risk estimates were pooled using a DerSimonian and Laird random-effects model, and adjusted hazards ratios (HRs) with corresponding 95% confidence interval (CI) were reported. The modified Newcastle-Ottawa Quality Assessment Scale (NOS) was used to examine the risk of bias. Results: Of 98 articles screened, four studies comprising 4,727,600 participants were included in the systematic review and two studies in the meta-analysis. Included studies had high quality and low risk of bias according to NOS. The pooled analysis revealed a significant risk of new onset IBD (HR: 1.28, 95% CI: 1.04–1.53, I2 = 74.9%), particularly Crohn’s disease (HR: 1.75, 1.59–1.92, I2 = 0), following bariatric surgery, but no significant risk of ulcerative colitis (HR: 0.93, 0.75–1.11, I2 = 11.5%). Conclusions: This meta-analysis found that bariatric surgery was associated with a higher risk of developing Crohn’s disease. Patients should be counseled on IBD risk pre-surgery, and symptomatic patients should be evaluated post-surgery to enable early diagnosis and management.
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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".