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Record W2895933668 · doi:10.1099/mgen.0.000227

Genomic epidemiology of clinical Campylobacter spp. at a single health trust site

2018· article· en· W2895933668 on OpenAlexfundno aff
Steven Dunn, Ben Pascoe, James Turton, Vicki M. Fleming, Mathew Diggle, Samuel K. Sheppard, Alan McNally, Georgina Manning

Bibliographic record

VenueMicrobial Genomics · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilMedical Research CouncilPublic Health EnglandTrent UniversityNottingham Trent University
KeywordsCampylobacteriosisCampylobacterTypingBiologyLocus (genetics)Multilocus sequence typingGeneticsEpidemiologySingle-nucleotide polymorphismLineage (genetic)PopulationWhole genome sequencingSNPGenomeMedicineEnvironmental healthGenotypeGenePathologyBacteria

Abstract

fetched live from OpenAlex

Campylobacter is the leading cause of bacterial enteritis in the developed world, and infections with the organism are largely sporadic in nature. Links between sporadic cases have not been established, with the majority of infections thought to be caused by genetically distinct isolates. Using a read-mapping approach, 158 clinical isolates collected during 2014 from the greater Nottinghamshire area were analysed to assess the local population structure and investigate potential case linkages between sporadic cases of campylobacteriosis. Four instances (2.5 %) of case linkage were observed across the dataset. This study demonstrates that case linkage does occur between sporadic Campylobacter infections, and provides evidence that a dual multi-locus sequence typing/within-lineage single nucleotide polymorphism typing approach to Campylobacter genomic epidemiology provides a benefit to public-health investigations.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.284
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.101
GPT teacher head0.324
Teacher spread0.223 · 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 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

Citations25
Published2018
Admission routes1
Has abstractyes

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