Seafood‐borne parasitic diseases in Australia: are they rare or underdiagnosed?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".