Cutaneous adaptive immunity and uraemia: a narrative review
Bibliographic record
Abstract
Chronic kidney disease affects 1 in 10 people globally, with a prevalence twenty times that of cancer. A subset of individuals will progress to end-stage renal disease (ESRD) where renal replacement therapy is required to maintain health. Cutaneous disease, including xerosis and pruritus, are endemic amongst patients with ESRD. In the uraemia-associated immune deficiency of ESRD, impaired circulating immune responses contribute to increased infection risk and poorer vaccination response. Clinical manifestations of dysregulated adaptive immunity within the skin have been well-described and have been posited to play a role in cutaneous features of ESRD. However, our understanding of the mechanisms by which adaptive immunity within the skin is affected by uraemia is relatively limited. We provide an overview of how the cutaneous adaptive immune system is impacted both directly and indirectly by uraemia, highlighting that much work has been extrapolated from the circulating immune system and often has not been directly evaluated in the skin compartment. We identify knowledge gaps which may be addressed by future research. Ultimately, greater understanding of these pathways may facilitate novel therapeutic approaches to ameliorate widespread cutaneous symptomatology in ESRD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".