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Record W4206932154 · doi:10.5578/mb.20229906

İshalli Hastalarda Norovirüs Sıklığının ve Farklı Tanı Yöntemlerinin Duyarlılıklarının Belirlenmesi

2022· article· tr· W4206932154 on OpenAlexaboutno aff
Cihan Yeşiloğlu, Betigül Öngen

Bibliographic record

VenueMikrobiyoloji Bulteni · 2022
Typearticle
Languagetr
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsNorovirusMedicineGold standard (test)DiarrheaImmunoassayVirologyInternal medicineGastroenterologyOutbreakImmunology

Abstract

fetched live from OpenAlex

Norovirus is one of the viruses that cause gastroenteritis in humans, characterized by symptoms of vomiting and diarrhea. The prevalence of norovirus, known as the leading cause of epidemic gastroenteritis, is remarkable in sporadic cases. Easy-to-apply diagnostic methods based on antigen detection such as enzyme immunoassay (EIA) and immunochromatography (ICG) used to diagnose norovirus infections generally have high specificity rates but lower sensitivity rates that can change according to the conditions. In this study it was aimed to determine the prevalence of norovirus and other gastroenteritis causative viruses in diarrheal patients and determine the sensitivity and specificity rates of EIA and ICG methods in sporadic cases by the chosen gold standard molecular reference method real-time reverse transcriptase polymerase chain reaction (rRT-PCR). In this study, 184 stool samples that met the study criteria and sent to İstanbul University İstanbul Faculty of Medicine Medical Microbiology Laboratory for routine bacteriological culture between January-July 2018 were included. All samples were evaluated with diagnostic kits BD MAX Enteric Viral Panel (Becton Dickinson, Canada) for rRT-PCR, RIDASCREEN Norovirus 3rd Generation (C1401) (R-Biopharm, Germany) for EIA, RIDAQUICK Norovirus Test (N1402) (R-Biopharm, Germany) for ICG, according to the manufacturer instructions. In terms of the presence of norovirus in 184 stool samples, 7 (3.8%) positive results were obtained by EIA method, 8 (4.3%) by ICG method, and 14 (7.6%) by rRT-PCR method. By accepting the rRT-PCR as a reference method, the sensitivity and specificity of the EIA method were determined as 50% and 100% and of the ICG method as 57% and 100%, respectively. The numbers and percentages of positivity for rotavirus, sapovirus, astrovirus and adenovirus, including coinfections were 30 (16.3%), 5 (2.7%), 2 (1%), 1 (0.5%), respectively. In this study, it was determined that norovirus, alone or together with other viral agents is frequently detected in patients with diarrhea and there was no difference in the frequency between age groups and genders. This causative agent which is not routinely investigated in our country should be considered when evaluating patients with diarrhea and/or vomiting. It seems that EIA and ICG methods are easy to apply, have high specificity but have limited sensitivity in sporadic cases in the diagnosis of norovirus. It is thought that the detection of the true frequency of norovirus and high sensitivity rates can only be achieved by preferring rRT-PCR in the diagnosis. It would be useful for the laboratories to choose the method to be used in the diagnosis of norovirus according to the characteristics of the cases and diagnostic methods.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.027
GPT teacher head0.291
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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