Prevalence of small intestinal bacterial overgrowth in intestinal failure syndrome: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Patients with intestinal failure (IF) have abnormal intestinal anatomy, secretion, and dysmotility, which impairs intestinal homeostatic mechanisms and may lead to small intestinal bacterial overgrowth (SIBO). We conducted a systematic review and meta-analysis to determine the prevalence of SIBO in patients with IF and to identify risk factors for SIBO. METHODS: MEDLINE (PubMed) and Embase electronic databases were searched from inception to December 2023 for studies that reported the prevalence of SIBO in IF. The prevalence rates, odds ratio (OR), and 95% confidence intervals of SIBO in IF and the risk factors for SIBO in IF were calculated using random effects model. RESULTS: = 80.9, P = 0.0001). SIBO prevalence was sixfold higher in patients with IF who received parenteral nutrition (PN) compared with IF patients not on PN (OR = 6.0, 95% CI 3.0-11.9, P = 0.0001). Overall, the prevalence of SIBO in patients with IF using PPI/acid-suppressing agents (72.0%, 95% CI 57.5-83.8) was numerically higher compared with IF patients not using these agents (47.6%, 95% CI 25.7-70.2). CONCLUSIONS: This systematic review and meta-analysis suggests that there is an increased risk of SIBO in patients with IF and that PN, and potentially, the use of PPI/acid-suppressing agents is risk factors for SIBO development in patients with IF. However, the quality of evidence is low and can be attributed to lack of case-control studies and clinical heterogeneity seen in the studies.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".