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Record W2802305614 · doi:10.1017/s0950268818001000

Risk factors associated with seropositivity to California serogroup viruses in humans and pet dogs, Quebec, Canada

2018· article· en· W2802305614 on OpenAlexafffundabout
Jean-Philippe Rocheleau, Pascal Michel, L. Robbin Lindsay, Michael Drebot, Antonia Dibernardo, N. H. Ogden, A. Fortin, Julie Arsenault

Bibliographic record

VenueEpidemiology and Infection · 2018
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité de MontréalPublic Health Agency of CanadaCegep de Saint Hyacinthe
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsVirologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Jamestown Canyon and snowshoe hare viruses are two emerging human pathogens associated with cases of neuroinvasive disease in North America. This study aimed to identify environmental and individual risk factors for seropositivity to these arboviruses in humans and pet dogs from Québec, Canada, 2012-2014. In humans, areas with moderate densities of white-tailed deer (Odocoileus virginianus) were associated with higher odds of seropositivity compared with areas with low densities of white-tailed deer (OR 2.50, P = 0.009) and odds of seropositivity were higher in males than in females (OR 2.03, P = 0.016). Among humans reporting more than 10 mosquito bites weekly, the odds of being seropositive were 4.44 times higher (P = 0.004) for people living in hardwood forested areas. Exposure to areas with coniferous forests was identified as the main environmental risk factor for seroconversion in dogs (OR 2.39, P = 0.04). These findings may help target further public health research, diagnostic and surveillance efforts 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 score0.014
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.304
Teacher spread0.274 · 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

Citations15
Published2018
Admission routes3
Has abstractyes

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