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Record W2118846302 · doi:10.1186/s12985-014-0224-x

Multiplex PCR analysis of clusters of unexplained viral respiratory tract infection in Cambodia

2014· article· en· W2118846302 on OpenAlexaff
Nary Ly, Rafal Tokarz, Nischay Mishra, Stephen Sameroff, Komal Jain, Agus Rachmat, Ung Sam An, Steven W. Newell, Dustin Harrison, W. Ian Lipkin

Bibliographic record

VenueVirology Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Allergy and Infectious DiseasesDefense Threat Reduction AgencyNational Institutes of HealthUnited States Agency for International DevelopmentU.S. Department of Defense
KeywordsRhinovirusEnterovirusVirologyBiologyVirusHuman bocavirusRespiratory systemRespiratory tractRespiratory tract infectionsMultiplex polymerase chain reactionMultiplexPathogenPicornavirusImmunologyPolymerase chain reactionGeneBioinformaticsGenome

Abstract

fetched live from OpenAlex

BACKGROUND: Fevers of unknown origin constitute a substantial disease burden in Southeast Asia. In majority of the cases, the cause of acute febrile illness is not identified. METHODS: We used MassTag PCR, a multiplex assay platform, to test for the presence of 15 viral respiratory agents from 85 patients with unexplained respiratory illness representing six disease clusters that occurred in Cambodia between 2009 and 2012. RESULTS: We detected a virus in 37 (44%) of the cases. Human rhinovirus, the virus detected most frequently, was found in both children and adults. The viruses most frequently detected in children and adults, respectively, were respiratory syncytial virus and enterovirus 68. Sequence analysis indicated that two distinct clades of enterovirus 68 were circulating during this time period. CONCLUSIONS: This is the first report of enterovirus 68 in Cambodia and contributes to the appreciation of this virus as an important respiratory pathogen.

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)0.002
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 score0.137
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.357
Teacher spread0.318 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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