Enteric dysbiosis in liver and kidney transplant recipients: a systematic review
Bibliographic record
Abstract
Several factors mediate intestinal microbiome (IM) alterations in transplant recipients, including immunosuppressive (IS) and antimicrobial drugs. Studies on the structure and function of the IM in the post-transplant scenario and its role in the development of metabolic abnormalities, infection, and cancer are limited. We conducted a systematic review to study the taxonomic changes in liver (LT) and kidney (KT) transplantation, and their potential contribution to post-transplant complications. The review also includes pre-transplant taxa, which may play a critical role in microbial alterations post-transplant. Two reviewers independently screened articles, and assessed risk of bias. The review identified 13 clinical studies, which focused on adult kidney and liver transplant recipients. Patient characteristics and methodologies varied widely between studies. Ten studies reported increased an abundance of opportunistic pathogens (Enterobacteriaceae, Enterococcaceae, Fusobacteriaceae, and Streptococcaceae) followed by butyrate-producing bacteria (Lachnospiraceae and Ruminococcaceae) in nine studies in post-transplant conditions. The current evidence is mostly based on observational data and studies with no proof of causality. Therefore, further studies exploring the bacterial gene functions rather than taxonomic changes alone are in demand to better understand the potential contribution of the IM in post-transplant complications.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".