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Record W2026450397 · doi:10.4269/ajtmh.13-0749

People, Pets, and Parasites: One Health Surveillance in Southeastern Saskatchewan

2014· article· en· W2026450397 on OpenAlexafffundabout
Janna M. Schurer, Momar Ndao, Helen Quewezance, Stacey A. Elmore, Emily Jenkins

Bibliographic record

VenueAmerican Journal of Tropical Medicine and Hygiene · 2014
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsMontreal General Hospital
FundersNatural Sciences and Engineering Research Council of CanadaBayer CanadaCanadian Institutes of Health ResearchSaskatchewan Health Research FoundationMcGill University Health CentreUniversity of SaskatchewanMcGill University
KeywordsEnvironmental healthMedicineGeography

Abstract

fetched live from OpenAlex

Residents of remote and Indigenous communities might experience higher exposure to some zoonotic parasites than the general North American population. Human sero-surveillance conducted in two Saulteaux communities found 113 volunteers exposed as follows: Trichinella (2.7%), Toxocara canis (4.4%), Echinococcus (4.4%), and Toxoplasma gondii (1.8%). In dogs, 41% of 51 fecal samples were positive for at least one intestinal parasite, 3% of 77 were sero-positive for Borrelia burgdorferi, and 21% of 78 for T. gondii. Echinococcus exposure was more likely to occur in non-dog owners (odds ratio [OR]: 11.4, 95% confidence interval [CI]: 1.2-107, P = 0.03); while T. canis was more likely to occur in children (ages 4-17) (OR: 49, 95% CI: 3.9-624; P = 0.003), and those with a history of dog bites (OR: 13.5, 95% CI: 1.02-179; P = 0.048). Our results emphasize the use of dogs as sentinels for emerging pathogens such as Lyme disease, and the need for targeted surveillance and intervention programs tailored for parasite species, cultural groups, and communities.

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 score0.290
Threshold uncertainty score0.584

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.015
GPT teacher head0.295
Teacher spread0.280 · 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

Citations42
Published2014
Admission routes3
Has abstractyes

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