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Record W4378909063 · doi:10.14745/ccdr.v49i05a02

Evolution of nucleic acid amplification testing across Canada as observed through the Canadian Laboratory Response Network's SARS-CoV-2 Proficiency Test Program, May 2020 to June 2021

2023· article· en· W4378909063 on OpenAlexafffundvenueabout
Charlene Ranadheera, Kym Antonation, Cindi R. Corbett

Bibliographic record

VenueCanada Communicable Disease Report · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsPublic Health Agency of Canada
FundersBritish Columbia Centre for Disease ControlPublic Health AgencyPublic Health Agency of CanadaAlberta Precision LaboratoriesNorthwestern University
KeywordsPandemicNucleic acid testTest (biology)Coronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Nucleic Acid Amplification TestsPublic healthDiagnostic testMedicineEnvironmental healthVirologyBiologyDiseaseEmergency medicineNursingPathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

To help accommodate the surge in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical testing due to the coronavirus disease 2019 pandemic, the decentralization of testing from provincial public health laboratories to regional laboratories and private facilities was necessary. To further support the growing number of test sites in Canada, the National Microbiology Laboratory developed a proficiency test program for the detection of SARS-CoV-2 using nucleic acid amplification tests and administered it under an arm of the Canadian Laboratory Response Network (CLRN). Since its conception in May 2020, CLRN has conducted three proficiency test schemes, from May 2020 to June 2021, and has observed an increase in participation of more than 400%. This article will explore the evolution of CLRN's SARS-CoV-2 Proficiency Test Program and its support of the Canadian pandemic response.

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)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.068
GPT teacher head0.330
Teacher spread0.262 · 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.

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

Citations1
Published2023
Admission routes4
Has abstractyes

Explore more

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