<scp>CD4</scp> <sup>+</sup> <scp>CD25</scp> <sup>+</sup> <scp>CD39</scp> <sup>+</sup> Regulatory T Cells as Novel Diagnostic Biomarkers for Catheter‐Related Bloodstream Infections in Hemodialysis Patients
Bibliographic record
Abstract
INTRODUCTION: Tregs as diagnostic biomarkers for CRBSI in HD patients, (2) compare the reliability of dialysis bloodline cultures versus peripheral venipuncture cultures, and (3) determine optimal fever thresholds for CRBSI prediction. METHODS: In this prospective cohort study, we enrolled 87 HD patients with suspected CRBSI (42 confirmed CRBSI, 45 non-CRBSI controls). Treg frequencies were quantified using flow cytometry. Paired blood cultures were obtained simultaneously from dialysis bloodlines and peripheral veins. CRBSI was confirmed using CDC criteria (≥ 3-fold higher colony count or ≥ 2-h earlier positivity in catheter-derived cultures). FINDINGS: CRBSI patients showed markedly elevated Treg frequencies (14.1% ± 4.5% vs. 3.3% ± 2.8%, p < 0.001) with outstanding diagnostic accuracy (AUC 0.974, 98.3% sensitivity, 96.3% specificity). Dialysis bloodline cultures demonstrated excellent concordance with peripheral cultures (92% agreement, κ = 0.88). Fever > 38.0°C strongly predicted CRBSI (OR 18.67, p < 0.001; 85% specificity). The diagnostic triad of Tregs > 10%, CRP > 50 mg/L, and fever > 38.0°C achieved exceptional discrimination (AUC 0.93). DISCUSSION: Tregs represent a novel, high-performance biomarker for CRBSI. Combined with validated dialysis line cultures and fever thresholds, they enable rapid diagnosis and early intervention, offering a practical alternative to current culture-dependent approaches in hemodialysis patients.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".