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Record W3018407240 · doi:10.1128/jcm.01841-19

Performance of a Modified Two-Tiered Testing Enzyme Immunoassay Algorithm for Serologic Diagnosis of Lyme Disease in Nova Scotia

2020· article· en· W3018407240 on OpenAlexaffabout
Ian Davis, Shelly McNeil, Wanda Allen, Donna MacKinnon‐Cameron, L. Robbin Lindsay, K Bernat, Antonia Dibernardo, Jason J. LeBlanc, Todd F. Hatchette

Bibliographic record

VenueJournal of Clinical Microbiology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsPublic Health Agency of CanadaNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsLyme diseaseSerologyImmunoassayBorrelia burgdorferiNova scotiaVirologyGold standard (test)MedicineLYMEAlgorithmImmunologyBiologyAntibodyInternal medicineComputer scienceGeography

Abstract

fetched live from OpenAlex

Compared to the standard two-tiered testing (STTT) algorithm for Lyme disease serology using an enzyme immunoassay (EIA) followed by Western blotting, data from the United States suggest that a modified two-tiered testing (MTTT) algorithm employing two EIAs has improved sensitivity to detect early localized Borrelia burgdorferi infections without compromising specificity. From 2011 to 2014, in the Canadian province of Nova Scotia, where Lyme disease is hyperendemic, sera submitted for Lyme disease testing were subjected to a whole-cell EIA, followed by C6 EIA and subsequently IgM and/or IgG immunoblots on sera with EIA-positive or equivocal results.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.068
GPT teacher head0.333
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2020
Admission routes2
Has abstractyes

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