Longitudinal transkingdom gut microbial approach towards decompensation in outpatients with cirrhosis
Bibliographic record
Abstract
Objective First decompensation development is a critical milestone that needs to be predicted. Transkingdom gut microbial interactions, including archaeal methanogens, may be important targets and predictors but a longitudinal approach is needed. Design Cirrhosis outpatients who provided stool twice were included. Group 1: compensated, group 2: 1 decompensation (decomp), group 3: >1 decompensationwere followed and divided into those who remained stable or decompensated. Bacteria, viral and archaeal presence, α/β diversity and taxa changes over time adjusted for clinical variables were analysed. Correlation networks between kingdoms were analysed. Results 157 outpatients (72 group 1, 33 group 2 and 52 group 3) were followed and 28%–47% developed outcomes. Baseline between those who remained stable/developed outcome: While no α/β diversity differences were seen, commensals were lower and pathobionts were higher in those who decompensated. After decompensation: those experiencing their first decompensation showed greater decrease in α/β-diversity, bacterial change (↑ Lactobacillus spp, Streptococcus parasanguinis and ↓ beneficial Lachnospiraceae and Eubacterium hallii ) and viral change (↑Siphoviridae, ↓ Myoviridae) versus those with further decompensation. Archaea: 19% had Methanobacter brevii, which was similar between/within groups. Correlation networks: Baseline archaeal-viral-bacterial networks were denser and more homogeneous in those who decompensated versus the rest. Archaea-bacterial correlations collapsed post first decompensation. Lactobacillus phage Lc Nu and C2-like viruses were negatively linked with beneficial bacteria. Conclusion In this longitudinal study of cirrhosis outpatients, the greatest transkingdom gut microbial changes were seen in those reaching the first decompensation, compared with subsequent decompensating events. A transkingdom approach may refine prediction and provide therapeutic targets to prevent cirrhosis progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".