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Record W4290672784 · doi:10.1038/s41551-022-00919-w

A lab-on-a-chip for the concurrent electrochemical detection of SARS-CoV-2 RNA and anti-SARS-CoV-2 antibodies in saliva and plasma

2022· article· en· W4290672784 on OpenAlexfundno aff
Devora Najjar, Joshua Rainbow, Sharma T. Sanjay, Pawan Jolly, Helena de Puig, Mohamed Yafia, Nolan Durr, Hani Sallum, Galit Alter, Jonathan Z. Li, Xu G. Yu, David R. Walt, Joseph A. Paradiso, Pedro Estrela, James J. Collins, Donald E. Ingber

Bibliographic record

VenueNature Biomedical Engineering · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Institute of Dental and Craniofacial ResearchNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesNational Institute of Nursing ResearchNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Institute on AgingHarvard UniversityFonds de recherche du Québec – Nature et technologiesNational Institute on Drug AbuseNatural Environment Research CouncilCenter for AIDS Research, University of WashingtonNational Institutes of HealthPaul G. Allen Frontiers GroupNational Cancer InstituteRagon Institute of MGH, MIT and HarvardHarvard University Center for AIDS ResearchHansjörg Wyss Institute for Biologically Inspired Engineering, Harvard UniversityMassachusetts Consortium on Pathogen Readiness
KeywordsSalivaAntibodyVirologyRNACoronavirusVirusImmune systemAntigenLoop-mediated isothermal amplificationSerologyBiologyCoronavirus disease 2019 (COVID-19)MedicineImmunologyDiseaseGeneInfectious disease (medical specialty)Biochemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.005

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 head0.020
GPT teacher head0.292
Teacher spread0.272 · 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

Citations276
Published2022
Admission routes1
Has abstractno

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