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Record W4256488678 · doi:10.1177/1040638717724837

Evaluation of a PCR assay on overgrown individual fecal samples cultured for <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i>

2017· article· en· W4256488678 on OpenAlexaff
Saray J Rangel, Juan Carlos Arango‐Sabogal, Olivia Labrecque, Julie Paré, Julie‐Hélène Fairbrother, Sébastien Buczinski, Jean‐Philippe Roy, Geneviève Côté, Vincent Wellemans, Gilles Fecteau

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2017
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversité de MontréalMinistère de l'Agriculture, des Pêcheries et de l'AlimentationCanadian Food Inspection Agency
Fundersnot available
KeywordsParatuberculosisMycobacterium avium subsp. paratuberculosisFecesMycobacteriumMicrobiologyBiologyPolymerase chain reactionVeterinary medicineBacteriaMedicineGeneticsGene

Abstract

fetched live from OpenAlex

Microbial overgrowth can interfere with Mycobacterium avium subsp. paratuberculosis (MAP) growth and detection. We estimated the percentage of positive samples by PCR performed on the incubated media of individual fecal samples classified as non-interpretable (NI) by bacteriologic culture of liquid media. A total of 262 liquid cultures declared NI and 88 samples declared negative were included in the study. MAP DNA was detected in 7 NI samples (2.7%; 95% CI: 1.1–5.4%) and in 1 negative sample (1.1%; 95% CI: 0.3–6.2%). The PCR allowed the detection of MAP-positive samples that had been missed in the initial bacteriologic culture. However, the benefit of these few additional positive results must be weighed against the additional costs incurred. Using PCR to classify overgrown cultures optimizes the detection process and eliminates the NI outcome.

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.147
GPT teacher head0.377
Teacher spread0.230 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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