Gut Microbiota Interventions to Retain Residual Kidney Function
Bibliographic record
Abstract
Residual kidney function for patients with chronic kidney disease (CKD) is associated with better quality of life and outcome; thus, strategies should be implemented to preserve kidney function. Among the multiple causes that promote kidney damage, gut dysbiosis due to increased uremic toxin production and endotoxemia need attention. Several strategies have been proposed to modulate the gut microbiota in these patients, and diet has gained increasing attention in recent years since it is the primary driver of gut dysbiosis. In addition, medications and faecal transplantation may be valid strategies. Modifying gut microbiota composition may mitigate chronic kidney damage and preserve residual kidney function. Although various studies have shown the influential role of diet in modulating gut microbiota composition, the effects of this modulation on residual kidney function remain limited. This review discusses the role of gut microbiota metabolism on residual kidney function and vice versa and how we could preserve the residual kidney function by modulating the gut microbiota balance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".