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Record W2149116072 · doi:10.1128/jcm.03686-14

Detection of Enterovirus D68 in Canadian Laboratories

2015· article· en· W2149116072 on OpenAlexaffabout
Todd F. Hatchette, Steven J. Drews, Elsie Grudeski, Tim Booth, Christine Martineau, Kerry Dust, Richard Garceau, Jonathan B. Gubbay, Tim Karnauchow, Mel Krajden, Paul N. Levett, Tony Mazzulli, Ryan McDonald, A. McNabb, Samira Mubareka, Robert Needle, Astrid Petrich, Susan E. Richardson, Candy Rutherford, Marek Smieja, Raymond Tellier, Graham Tipples, Jason J. LeBlanc

Bibliographic record

VenueJournal of Clinical Microbiology · 2015
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsHospital for Sick ChildrenUniversity of CalgaryMount Sinai HospitalMinistry of HealthChildren's Hospital of Eastern OntarioSunnybrook Health Science CentreBC Centre for Disease ControlPublic Health OntarioSaskatchewan Disease Control LaboratoryUniversity of AlbertaHealth Sciences CentreUniversity Health NetworkCapital District Health AuthoritySt. Joseph’s Healthcare HamiltonInstitut National de Santé Publique du QuébecUniversity of OttawaDr. Georges-L.-Dumont University Hospital CentreProvincial Laboratory of Public HealthSt. John’s Health Sciences CentreUniversity of TorontoDalhousie University
Fundersnot available
KeywordsEnterovirusVirologyEnterovirus InfectionsBiologyMedicineVirus

Abstract

fetched live from OpenAlex

The recent emergence of a severe respiratory disease caused by enterovirus D68 prompted investigation into whether Canadian hospital and provincial laboratories can detect this virus using commercial and laboratory-developed assays. This study demonstrated analytical sensitivity differences between commercial and laboratory-developed assays for the detection of enterovirus D68.

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)0.005
metaresearch head score (Gemma)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.438
Teacher spread0.342 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

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