MétaCan
Menu
Back to cohort
Record W4285086369 · doi:10.1128/spectrum.00639-22

Avoiding False-Positive SARS-CoV-2 Rapid Antigen Test Results with Point-of-Care Molecular Testing on Residual Test Buffer

2022· article· en· W4285086369 on OpenAlexaff
Jason J. LeBlanc, Gregory R. McCracken, Barbara Goodall, Todd F. Hatchette, Lisa Barrett, John J. Ross, Ross Davidson, Glenn Patriquin

Bibliographic record

VenueMicrobiology Spectrum · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsPoint-of-care testingMedicineAsymptomaticNucleic Acid Amplification TestsFalse positive paradoxSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyNucleic acid testDiagnostic testConcordanceFalse Negative ReactionsCoronavirus disease 2019 (COVID-19)DiseaseInternal medicineImmunologyEmergency medicineInfectious disease (medical specialty)Statistics

Abstract

fetched live from OpenAlex

During the COVID-19 pandemic, rapid antigen tests have been widely used for the detection of SARS-CoV-2. These simple devices allow rapid test results. However, false-positive results may occur. As such, individuals with positive rapid tests often must return to testing centers to have a second swab collected, which is then transported to a specialized laboratory for confirmation using molecular tests. As an alternative to requiring a repeat visit and a prolonged turn-around time for result confirmation, this project evaluated whether the leftover material from rapid antigen tests could be confirmed directly on a portable point-of-care molecular instrument. Using this approach, molecular confirmation of positive antigen tests could be performed in less than 15 min, and the results were equivalent to laboratory-based confirmation. This procedure eliminates the need for individuals to return to testing centers following a positive rapid antigen test and ensures accurate antigen test results through on-site confirmation.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.023
GPT teacher head0.262
Teacher spread0.240 · 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 designBench or experimental
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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMicrobiology SpectrumSame topicSARS-CoV-2 detection and testingFrench-language works237,207