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Record W3196384035 · doi:10.1128/spectrum.01098-21

Development of a Multiplex Real-Time PCR Assay for Mycobacterium bovis BCG and Validation in a Clinical Laboratory

2021· article· en· W3196384035 on OpenAlexafffund
Shannon C. Duffy, Manigandan Venkatesan, Shubhada K. Chothe, Indira Poojary, Valsan Philip Verghese, Vivek Kapur, Marcel A. Behr, Joy Sarojini Michael

Bibliographic record

VenueMicrobiology Spectrum · 2021
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchGovernment of CanadaBill and Melinda Gates Foundation
KeywordsMycobacterium bovisMultiplexMultiplex polymerase chain reactionReal-time polymerase chain reactionBiologyComputational biologyMycobacteriumMicrobiologyMedicinePolymerase chain reactionGeneticsMycobacterium tuberculosisPathologyTuberculosisGeneBacteria

Abstract

fetched live from OpenAlex

Vaccination against tuberculosis with bacillus Calmette-Guérin (BCG) can lead to adverse events, including a rare but life-threatening complication of disseminated BCG. This complication often occurs in young children with immunodeficiencies and is associated with an ∼60% mortality rate. A rapid method of reliably identifying BCG infection is important because BCG requires treatment unique to tuberculosis. BCG is resistant to the first-line antituberculosis drug pyrazinamide. Additionally, diagnosis of BCG disease would lead to further investigation of a possible underlying immune condition. We have developed a diagnostic assay to identify BCG which improves upon previously published methods and can reliably identify BCG from bacterial culture or directly from infected tissue. This assay can also differentiate between strains of BCG, which have been suggested to be associated with different rates of adverse events. This assay was validated on 19 clinical isolates collected at Christian Medical College in Vellore, India.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.345
Teacher spread0.312 · 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 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

Citations11
Published2021
Admission routes2
Has abstractyes

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