MétaCan
Menu
Back to cohort
Record W2197451746 · doi:10.1017/s0950268815002186

A comparison of exposure to risk factors for giardiasis in non-travellers, domestic travellers and international travellers in a Canadian community, 2006–2012

2015· article· en· W2197451746 on OpenAlexaffabout
Alexandra Swirski, David L. Pearl, Andrew S. Peregrine, Katarina Pintar

Bibliographic record

VenueEpidemiology and Infection · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsPublic Health Agency of CanadaUniversity of Guelph
Fundersnot available
KeywordsEnvironmental healthMedicineGeography

Abstract

fetched live from OpenAlex

The purpose of this study is to determine how demographic and exposure factors related to giardiasis vary between travel and endemic cases. Exposure and demographic data were gathered by public health inspectors from giardiasis cases reported from the Region of Waterloo from 2006 to 2012. Logistic regression models were fit to assess differences in exposure to risk factors for giardiasis between international travel-related cases and Canadian acquired cases while controlling for age and sex. Multinomial regression models were also fit to assess the differences in risk profiles between international and domestic travel-related cases and endemic cases. Travel-related cases (both international and domestic) were more likely to go camping or kayaking, and consume untreated water compared to endemic cases. Domestic travel-related cases were more likely to visit a petting zoo or farm compared to endemic cases, and were more likely to swim in freshwater compared to endemic cases and international travel-related cases. International travellers were more likely to swim in an ocean compared to both domestic travel-related and endemic cases. These findings demonstrate that travel-related and endemic cases have different risk exposure profiles which should be considered for appropriately targeting health promotion campaigns.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.068
GPT teacher head0.363
Teacher spread0.295 · 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.

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

Citations5
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueEpidemiology and InfectionSame topicParasitic Infections and DiagnosticsFrench-language works237,207