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Record W4414239514 · doi:10.3390/diagnostics15182353

Serum P-Cresyl Sulfate Is Associated with Peripheral Arterial Stiffness in Chronic Hemodialysis Patients

2025· article· en· W4414239514 on OpenAlexaff
Yahn-Bor Chern, Chih‐Hsien Wang, Chin‐Hung Liu, Hung-Hsiang Liou, Jen‐Pi Tsai, Bang‐Gee Hsu

Bibliographic record

VenueDiagnostics · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal function and acid-base balance
Canadian institutionsSt. Michael's Hospital
FundersBuddhist Tzu Chi Medical Foundation
KeywordsArterial stiffnessPeripheralHemodialysisConfidence intervalReceiver operating characteristicOdds ratioBiomarker

Abstract

fetched live from OpenAlex

Background/Objectives: Arterial stiffness is a major cardiovascular risk factor in patients with hemodialysis (HD). We conducted a cross-sectional study aimed at determining the relationship between serum p-Cresyl sulfate (PCS) and peripheral arterial stiffness (PAS), defined via the cardio-ankle vascular index (CAVI), in 110 patients receiving chronic HD. Methods: Participants were divided into PAS (CAVI ≥ 9.0) and control (CAVI < 9.0) groups. Serum PCS level was measured by high-performance liquid chromatography-mass spectrometry. Results: PAS was detected in 37 (33.6%) patients. The PAS patients were older and had higher SBP, more diabetes, and higher serum PCS and C-reactive protein (CRP) than the control group. Upon multivariate analysis, PAS was significantly associated with PCS (adjusted odds ratio: 1.238 per 1 mg/L increase, 95% confidence interval [CI]: 1.119–1.371, p < 0.001). The CAVI, advanced age, and CRP demonstrated a significant correlation with PCS, as evidenced by the correlation analysis conducted. Area under the receiver operating characteristic curve analysis showed that PCS had a good diagnostic value for PAS (AUC: 0.872, 95% CI: 0.805–0.939; p < 0.001), and the optimal cutoff value was 24.29 mg/L. Conclusions: PCS demonstrates great potential as a biomarker in the diagnosis of arterial stiffness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.241
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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