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Record W2823405166 · doi:10.3347/kjp.2018.56.3.215

An Overview of Fish-borne Nematodiases among Returned Travelers for Recent 25 Years– Unexpected Diseases Sometimes Far Away from the Origin

2018· review· en· W2823405166 on OpenAlexfundno aff
J. C. Eiras, Gilberto Cezar Pavanelli, Ricardo Massato Takemoto, Yukifumi Nawa

Bibliographic record

VenueKorean Journal of Parasitology · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaConselho Nacional de Desenvolvimento Científico e TecnológicoMinistério da Ciência, Tecnologia e InovaçãoMountain Equipment Co-operativeMinisterio de Economía y CompetitividadCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsAnisakisIncidence (geometry)Fish <Actinopterygii>CapillariaBiologyZoologyHelminthsFishery

Abstract

fetched live from OpenAlex

Along with globalization of traveling and trading, fish-borne nematodiases seems to be increasing in number. However, apart from occasional and sporadic case reports or mini-reviews of particular diseases in particular countries, an overview of fish-borne nematodiasis among travelers have never been performed. In this review, we gathered fishborne nematodiasis among travelers for recent 25 years by an extensive global literature survey using appropriate keywords, e.g. travelers diseases, human infection, anisakiasis, gnathostomiasis, capillariasis, sushi, sashimi, ceviche, Gnathostoma, Pseudoterranova, Anisakis, Capillaria, etc., as well as various combinations of these key words. The Internet search engines PubMed, Medline, Google and Googler Scholar were used as much as possible, and the references of every paper were checked in order to identify useful and reliable publications. The results showed unexpectedly high incidence of gnathostomiasis and low incidence of anisakidosis. The different incidence values of the infection with several fish-borne zoonotic nematode species are discussed, as well as some epidemiological aspects of the infections. The difficulties of differential diagnosis in non-endemic countries are emphasized. It is concluded that travelers must avoid risky behaviors which can lead to infection and that physicians and health authorities must advice travelers on the risks of eating behaviors during travel.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.749
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.428
Teacher spread0.345 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations47
Published2018
Admission routes1
Has abstractyes

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