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Record W2789685698 · doi:10.14745/ccdr.v42i08a01

Illness in Canadian travellers and migrants from Brazil: CanTravNet surveillance data, 2013–2016

2016· article· en· W2789685698 on OpenAlexaffvenueabout
AK Boggild, J Geduld, Michael Libman, C.P. Yansouni, AE McCarthy, Jan Hájek, Wayne Ghesquière, Jean Vincelette, Susan Kuhn, PJ Plourde, DO Freedman, Kevin C. Kain

Bibliographic record

VenueCanada Communicable Disease Report · 2016
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsUniversité de MontréalWinnipeg Regional Health AuthorityVancouver General HospitalUniversity of CalgaryCentre for Global Health ResearchHôpital Saint-LucIsland HealthUniversity of TorontoOttawa HospitalUniversity Health NetworkAlberta Children's HospitalMcGill UniversityUniversity of OttawaPublic Health Agency of CanadaToronto Public HealthUniversity of British ColumbiaPublic Health Ontario
Fundersnot available
KeywordsMedicineDengue feverChikungunyaRashDemographyPediatricsVirologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In light of the 2016 summer Olympic games it is anticipated that Canadian practitioners will require information about common illnesses that may affect travellers returning from Brazil. OBJECTIVE: To identify the demographic and travel correlates of illness among recent Canadian travellers and migrants from Brazil attending a network of travel health clinics across Canada. METHODS: Data was analyzed on returned Canadian travellers and migrants presenting to a CanTravNet site for care of an illness between June 2013 and June 2016. RESULTS: =0.0024]). INTERPRETATION: An epidemiologic approach to illness among returned Canadian travellers to Brazil can inform Canadian practitioners encountering both prospective and returned travellers to the Olympic games. Analysis showed that vector-borne illnesses such as dengue are common and even in this small group of travellers, both chikungunya and Zika virus were represented. It is extremely important to educate travellers about mosquito-avoidance measures in advance of travel to Brazil.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.255
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.022
GPT teacher head0.288
Teacher spread0.266 · 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

Citations3
Published2016
Admission routes3
Has abstractyes

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