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Record W2135112655 · doi:10.1186/1756-3305-7-183

The prevalence of Borrelia miyamotoi infection, and co-infections with other Borrelia spp. in Ixodes scapularis ticks collected in Canada

2014· article· en· W2135112655 on OpenAlexaffabout
Antonia Dibernardo, Tyler Cote, Nicholas H. Ogden, L. Robbin Lindsay

Bibliographic record

VenueParasites & Vectors · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsIxodes scapularisBorreliaBorrelia burgdorferiAnaplasma phagocytophilumBiologyTickIxodesLyme diseaseTick-borne diseaseVirologyrelapsing feverAmblyomma americanumIxodidaeImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Blacklegged ticks, Ixodes scapularis are vectors of the tick-borne pathogens Borrelia burgdorferi, Anaplasma phagocytophilum and Babesia microti. Recently, the I. scapularis-borne bacterium Borrelia miyamotoi has been linked to human illness in North America. The range of this tick is expanding in Canada which may increase the potential for human exposure to these agents. METHODS: In this study, 4938 I. scapularis ticks collected in 2012 were tested following a newly developed PCR-based testing protocol to determine the prevalence of infection with B. miyamotoi and other pathogens in I. scapularis in Canada. RESULTS: Borrelia miyamotoi was detected in blacklegged ticks from all provinces except Newfoundland, although the infection prevalence was low (<1%). There was significant variation among provinces in the prevalence of infection of ticks with B. burgdorferi and A. phagocytophilum, but not with B. miyamotoi. CONCLUSIONS: Given the widespread distribution of B. miyamotoi, infection due to this agent should be considered in patients who have been exposed to blacklegged ticks in Canada.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.215
Teacher spread0.211 · 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

Citations117
Published2014
Admission routes2
Has abstractyes

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