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Record W2034679551 · doi:10.2217/fmb.13.9

Rapid Antigen-Based Testing for Respiratory Syncytial Virus: Moving Diagnostics from Bench to Bedside?

2013· review· en· W2034679551 on OpenAlexaff
Caitlin Prendergast, Jesse Papenburg

Bibliographic record

VenueFuture Microbiology · 2013
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsMontreal Children's HospitalMcGill University
Fundersnot available
KeywordsPoint-of-care testingBronchiolitisVirologyImmunologyVirusHuman metapneumovirusBench to bedsideMedicineAntigenIntensive care medicinePneumoniaRespiratory systemDiagnostic testImmunofluorescenceRespiratory tract infectionsPediatricsAntibodyInternal medicine

Abstract

fetched live from OpenAlex

Respiratory syncytial virus (RSV) is the most important cause of infantile bronchiolitis and pneumonia. It is ubiquitous, with most children acquiring their primary infection within the first year of life and with subsequent reinfection occurring in all age groups. Clinically, RSV is virtually indistinguishable from other viral respiratory infections. Traditionally, the microbiologic diagnosis of RSV has been based on moderate to complex techniques performed in a laboratory (cell culture, nucleic acid amplification and immunofluorescence assays); however, rapid antigen-detection tests offer potential advantages associated with point-of-care testing. This review seeks to familiarize the readers with RSV rapid antigen-detection tests, describe their performance characteristics and comment on their strengths and weaknesses. The authors will discuss the impact of rapid RSV testing on clinical practice, with a look to the future of what the field ultimately requires of a point-of-care diagnostic technique.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.117
GPT teacher head0.393
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations52
Published2013
Admission routes1
Has abstractyes

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