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Record W2799303791 · doi:10.1111/imj.13786

Seafood‐borne parasitic diseases in Australia: are they rare or underdiagnosed?

2018· article· en· W2799303791 on OpenAlexfundno aff
Shokoofeh Shamsi, Harsha Sheorey

Bibliographic record

VenueInternal Medicine Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
FundersInstitute of Infection and Immunity
KeywordsMedicineFish <Actinopterygii>HelminthsEnvironmental healthDifferential diagnosisSerologyConsumption (sociology)ImmunologyPathologyFisheryBiology

Abstract

fetched live from OpenAlex

Australia is a multicultural country surrounded by water where seafood is regularly consumed. Literature suggests that some popular edible fish sold in fish markets may be infected with parasites transmissible to humans (notably, anisakids and other helminths); however the number of reported human cases due to these parasites is low. In this article we critically review topical publications to understand whether the low number of human infection is due to lack of expertise in Australia to identify and diagnose accurately seafood-borne parasitic infections. The risk these parasites pose to humans may be underestimated due to: (i) errors or inability of diagnosing these infections, primarily due to less sensitive and specific serological tests and misidentifying parasites without a taxonomist in the diagnostic team; and (ii) medical practitioners not being aware of these parasites or not considering them in the differential diagnosis even in patients with history of regular raw or undercooked seafood consumption.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0310.001

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 head0.041
GPT teacher head0.392
Teacher spread0.351 · 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

Citations68
Published2018
Admission routes1
Has abstractyes

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