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Record W2782428263 · doi:10.1089/vbz.2017.2179

Exposure to Rats and Rat-Associated <i>Leptospira</i> and <i>Bartonella</i> Species Among People Who Use Drugs in an Impoverished, Inner-City Neighborhood of Vancouver, Canada

2018· article· en· W2782428263 on OpenAlexaffabout
David A. McVea, Chelsea G. Himsworth, David M. Patrick, L. Robbin Lindsay, Michael Kosoy, Thomas Kerr

Bibliographic record

VenueVector-Borne and Zoonotic Diseases · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicLeptospirosis research and findings
Canadian institutionsAIDS VancouverProvidence Health CarePublic Health Agency of CanadaCanadian Wildlife FederationUniversity of British Columbia
Fundersnot available
KeywordsLeptospira interrogansEnvironmental healthDowntownLeptospiraRodentPsychological interventionBiologyGeographyMedicineDemographyVeterinary medicineEcologyPathologyLeptospirosis

Abstract

fetched live from OpenAlex

Rat infestations are common, particularly in impoverished, inner-city neighborhoods. However, there has been little research into the nature and consequences of rat exposure in these neighborhoods, particularly in Canada. In this study, we sought to characterize exposure to rats and rat-associated Leptospira interrogans and Bartonella tribocorum, as well as risk factors associated with exposure, in residents (n = 202) of the Downtown Eastside (DTES) neighborhood of Vancouver, Canada. There was no evidence of exposure to rat-associated L. interrogans but 6/202 (3.0%) of participants were exposed to B. tribocorum, which is known to be circulating among DTES rats. We also found that frequent and close rat exposure was common among DTES residents, and that this exposure was particularly associated with injection drug use and outdoor income-generating activities (e.g., drug dealing). These risk factors may be good targets for interventions geared toward effectively reducing rat exposure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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 teacher head, not a consensus.

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

Citations18
Published2018
Admission routes2
Has abstractyes

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