Mucosa-Associated Lymphoid Tissue Surgeries as a Possible Risk for Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Inflammatory bowel disease (IBD) is a group of chronic inflammatory gastrointestinal disorders that are caused by genetic susceptibility and environmental factors and affects a significant portion of the global population. The gut-associated lymphoid tissue (GALT) is known to play a crucial role in immune modulation and maintaining gut microbiota balance. Dysbiosis in the latter has a known link to IBD. Therefore, the increasing prevalence of adenoidectomy in children should be explored for its potential association with IBD. The objective of this paper was to assess the association between adenoid tissue removal and the risk of developing Crohns disease (CD) and ulcerative colitis (UC). Methods: We conducted a pooled meta-analysis to evaluate the extended clinical outcomes in patients who underwent appendicectomy and tonsillectomy compared to those who did not. Our approach involved systematically searching the PubMed database for relevant observational studies written in English. We followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines to collect data from various time periods, and to address the diversity in study results; we employed a random-effects analysis that considered heterogeneity. For outcomes, odds ratios (ORs) were pooled using a random-effects model. Results: Seven studies, out of a total of 114,537, met our inclusion criteria. Our meta-analysis revealed a significant association between appendicectomy and CD (OR: 1.57; 95% confidence interval (CI): 1.01 - 2.43; heterogeneity I 2 = 93%). Similarly, we found a significant association between tonsillectomy and CD (OR: 1.93; 95% CI: 0.96 - 3.89; I 2 = 62%). However, no significant association was observed between appendicectomy and UC (OR: 0.60; 95% CI: 0.24 - 1.47; I 2 = 96%), while a modest association was found between tonsillectomy and UC (OR: 1.24; 95% CI: 1.18 - 1.30; I 2 = 0%). Conclusions: In summary, we found that the trend of appendicectomy is linked to higher odds of CD, and tonsillectomy is more likely associated with increased odds for both CD and UC, with a risk of bias present. Gastroenterol Res. 2024;17(2):90-99 doi: https://doi.org/10.14740/gr1672
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".