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Record W2024939854 · doi:10.1139/w03-065

Prospective study of hepatitis C virus infection in hemodialysis patients by monthly analysis of HCV RNA and antibodies

2003· article· en· W2024939854 on OpenAlexvenueno aff
Regina Célia Moreira, João Renato Rebello Pinho, Jorge Fares, Isabel Takano Oba, Maria Regina Alves Cardoso, Cláudia Patara Saraceni, Celso Granato

Bibliographic record

VenueCanadian Journal of Microbiology · 2003
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsnot available
Fundersnot available
KeywordsHepatitis C virusHemodialysisSerologyIncidence (geometry)MedicineGenotypePopulationHepatitis CInternal medicineAntibodyVirologyHepacivirusImmunologyGastroenterologyVirusBiologyEnvironmental health

Abstract

fetched live from OpenAlex

The aims of this study were to (i) evaluate the prevalence and the incidence of hepatitis C virus (HCV) infection in hemodialysis patients in two different centers in São Paulo (Brazil), (ii) determine the time required to detect HCV infection among these patients by serology or PCR, (iii) establish the importance of alanine aminotransferase determination as a marker of HCV infection, and (iv) identify the HCV genotypes in this population. Serum samples were collected monthly for 1 year from 281 patients admitted to hospital for hemodialysis. Out of 281 patients, 41 patients (14.6%) were HCV positive; six patients seroconverted during this study (incidence = 3.1/1000 person-month). In 1.8% (5/281) of cases, RNA was detected before the appearance of antibodies (up to 5 months), and in 1.1% (3/281) of cases, RNA was the unique marker of HCV infection. The genotypes found were 1a, 1b, 3a, and 4a. The presence of genotype 4a is noteworthy, since it is a rare genotype in Brazil. These data pointed out the high prevalence and incidence of HCV infection at hemodialysis centers in Brazil and showed that routine PCR is fundamental for improving the detection of HCV carriers among patients undergoing hemodialysis.

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 score0.475
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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

Citations47
Published2003
Admission routes1
Has abstractyes

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