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Record W2623966028 · doi:10.5604/01.3001.0010.0277

Occult Hepatitis C Infection Among Hemodialysis Patients: A Prevalence Study

2017· article· en· W2623966028 on OpenAlexaff
Reza Naghdi, Mitra Ranjbar, Farah Bokharaei‐Salim, Hossein Keyvani, Shokoufeh Savaj, Shahrzad Ossareh, Amir Shirali, Amir Houshang Mohammad-Alizadeh

Bibliographic record

VenueAnnals of Hepatology · 2017
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicinePeripheral blood mononuclear cellOccultHemodialysisInternal medicineHepatitis CSerologyHepatitis C virusGastroenterologyImmunologyRNAAntibodyPathologyVirusGeneIn vitroBiology

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIM: Occult hepatitis C infection (OHCI) is the presence of HCV-RNA in the liver or peripheral blood mononuclear cells (PBMC) accompanying with negative serologic results. The aim of this study was to evaluate the prevalence of OHCI among Iranian chronic hemodialysis (HD) patients. MATERIAL AND METHODS: In this cross sectional study 200 chronic HD patients with negative HCV antibody enrolled the study. Blood sample of patients were obtained, followed by Polymerase Chain reaction (PCR) testing for detection of HCV RNA. Patients with positive serum HCV RNA were considered as manifest hepatitis C infection (MHCI). However, patients with negative serum HCV RNA underwent further tests on PBMCs for detection of OHCI. RESULTS: Serum HCV RNA was positive in 2 (1%) patients whom considered as MHCI, and 6 (3.03%) patients had positive PBMC HCV RNA. CONCLUSION: In conclusion, chronic HD patients have been considered as a high risk group for hepatitis C infection. The results of this study suggest that these patients are also at risk for OHCI. Furthermore, evaluating PBMCs to detect HCV RNA would be a sensitive diagnostic method to find OHCI patients.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.020
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.092
GPT teacher head0.413
Teacher spread0.321 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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