Appendectomy and the Risk of Microscopic Colitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: microscopic colitis (MC), a chronic intestinal inflammatory disorder characterised by persistent watery diarrhoea, is categorised into collagenous and lymphocytic subtypes. Recent studies suggest that appendectomy influences the risk of MC, although the evidence remains inconclusive. This meta-analysis of available research was conducted to clarify the relationship between appendectomy and MC risk. METHODS: in accordance with the PRISMA guidelines, a comprehensive search was conducted in the Web of Science, EMBASE, and PubMed up to January 2024, focusing on studies that explored the association between appendectomy and MC. Quality was assessed using the Newcastle-Ottawa Scale, with data synthesis using the DerSimonian and Laird random-effects model. Heterogeneity and potential biases were evaluated; subgroup analyses were performed to investigate specific associations. RESULTS: six studies were analysed, including one cohort and five case-control studies involving 85,845 participants. The combined analysis showed no significant link between appendectomy and MC risk (OR: 1.20, 95 % CI: 0.91-1.58), despite moderate heterogeneity (I² = 59 %). Subgroup analyses indicated potential associations in specific contexts. Notably, significant associations were found in subgroups based on MC subtypes (CC: OR 1.59, 95 % CI: 1.20-2.10; LC: OR 1.45, 95 % CI: 1.34-1.58), unadjusted ORs (OR 1.42, 95 % CI: 1.17-1.73), healthy control groups (OR 1.51, 95 % CI: 1.38-1.67) and studies using medical records for appendectomy history (OR 1.50, 95 % CI: 1.28-1.75). Other subgroup analyses did not yield significant results. CONCLUSION: this meta-analysis did not support a significant association between appendectomy and increased risk of MC. These findings highlight the need for further large-scale, prospective studies to explore this relationship in greater detail, considering the potential for nuanced interactions and the impacts of various confounding factors.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".