Postcholecystectomy Gut Microbiome Changes and the Clinical Impact: A Systematic Review With Narrative Synthesis
Bibliographic record
Abstract
BACKGROUND: Cholecystectomy (CCE) can affect the enterohepatic circulation of bile acids and result in gut microbiome changes. This systematic review aimed to clarify the effect of CCE on gut microbiome composition and its clinical impact. METHOD: A systematic search was conducted in PubMed, Web of Science, and Scopus, combining keywords such as "cholecystectomy" or "post-cholecystectomy" with "gut microbiome," "stool microbiome," or "gut dysbiosis." Data were extracted and synthesized using narrative review. Study quality was assessed using the Newcastle-Ottawa scale. RESULTS: A total of 1373 articles were screened and 14 studies were selected. Significant but inconsistent microbiome changes were reported. Changes were observed in alpha and beta diversity. At phylum level, an increase in Bacteroides and Ascomycota, decrease in Firmicutes, Actinomycetes, and Basidiomycota, and both increase and decrease in Fusobacteria were reported. At genus level, an increase in Prevotella and a decrease in Faecalibacterium were reported. In post-CCE diarrhea, decreased beta diversity, a decreased F/B ratio, an increase in Prevotella, an increase in Phocaeicola vulgatus, and a decrease in Prevotella copri were noted. For post-CCE syndrome, a higher abundance of Proteobacteria and decreased Firmicutes/Bacteroides (F/B) ratio were reported. A decreased relative abundance of Bifidobacterium longum subsp. longum from controls to CCE without colonic neoplasia to CCE with colonic neoplasia, and an increased abundance of Candida glabrata from controls, to CCE without colonic neoplasia and CCE with colonic neoplasia, were reported. CONCLUSION: Patients who underwent CCE had significant gut dysbiosis. However, current studies could not clarify the detailed gut microbial structural and functional changes associated with CCE.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".