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Record W2971104290 · doi:10.1038/s41598-019-47740-1

Evaluating the use of whole genome sequencing for the investigation of a large mumps outbreak in Ontario, Canada

2019· article· en· W2971104290 on OpenAlexaffabout
Patrick J. Stapleton, Alireza Eshaghi, C. Y. Seo, Sarah E. Wilson, Tara Harris, Shelley L. Deeks, Shelly Bolotin, Lee Goneau, Jonathan B. Gubbay, Samir N. Patel

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsResponse Biomedical (Canada)Public Health OntarioUniversity of Toronto
Fundersnot available
KeywordsOutbreakGenomeWhole genome sequencingMumps virusComputational biologyVirologyData scienceBiologyComputer scienceGeneticsGeneVirus

Abstract

fetched live from OpenAlex

In 2017 Ontario experienced the largest mumps outbreak in the province in 8 years, at a time when multiple outbreaks were occurring across North America. Of 259 reported cases, 143 occurred in Toronto, primarily among young adults. Routine genotyping of the small hydrophobic gene indicated that the outbreak was due to mumps virus genotype G. We performed a retrospective study of whole genome sequencing of 26 mumps virus isolates from early in the outbreak, using a tiling amplicon method. Results indicated that two of the cases were genetically divergent, with the remaining 24 cases belonging to two major clades and one minor clade. Phylogeographic analysis confirmed circulation of virus from each clade between Toronto and other regions in Ontario. Comparison with other genotype G strains from North America suggested that the presence of co-circulating major clades may have been due to separate importation events from outbreaks in the United States. A transmission network analysis performed with the software program TransPhylo was compared with previously collected epidemiological data. The transmission tree correlated with known epidemiological links between nine patients and identified new potential clusters with no known epidemiological links.

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 score0.344
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.102
GPT teacher head0.318
Teacher spread0.216 · 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

Citations29
Published2019
Admission routes2
Has abstractyes

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