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Record W3120749237 · doi:10.1016/j.ebiom.2020.103183

A breath of fresh air – the potential for COVID-19 breath diagnostics

2021· article· en· W3120749237 on OpenAlexfundno aff
Cristina E. Davis, Michael Schivo, Nicholas J. Kenyon

Bibliographic record

VenueEBioMedicine · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of Environmental Health SciencesNational Institutes of HealthCenter for Information Technology Research in the Interest of SocietyCanada Economic Development for Quebec RegionsTobacco-Related Disease Research Program
KeywordsFalse positive paradoxPandemicGold standard (test)MedicineCoronavirus disease 2019 (COVID-19)Public healthDiagnostic testSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Test (biology)Computer scienceVirologyRisk analysis (engineering)DiseasePathologyBiologyInfectious disease (medical specialty)Artificial intelligenceVeterinary medicine

Abstract

fetched live from OpenAlex

As the world continues to grapple with the ongoing SARS-CoV-2 pandemic, it remains clear that frequent and widespread virus testing is a valuable tool to understand disease spread and to guide public health actions by communities and governments. To date, most traditional diagnostic tests continue to rely on established polymerase chain reaction (PCR) technologies, which have proven to be quite robust as a tool for mass screening and remain the gold standard within modern medicine. When employed using standardized protocols, PCR typically has a high accuracy and high specificity (eg, low false positives and low false negatives).

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)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0060.007
Open science0.0020.004
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0200.008

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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 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

Citations27
Published2021
Admission routes1
Has abstractyes

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