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Record W2888047022 · doi:10.1017/pao.2018.14

Canine echinococcosis screening in foxhound hunts in England and Wales using coproantigen ELISA and coproPCR

2018· article· en· W2888047022 on OpenAlexaboutno aff
Wai San Lett, Belgees Boufana, Samia Lahmar, Helen Bradshaw, Thomas M.H. Walters, Arjen Brouwer, Alasdair R. Fraser, Deborah Maskell, Philip S. Craig

Bibliographic record

VenueParasitology Open · 2018
Typearticle
Languageen
FieldMedicine
TopicParasitic infections in humans and animals
Canadian institutionsnot available
FundersCharles Sturt UniversityLlywodraeth CymruUniversity of Salford Manchester
KeywordsEchinococcus granulosusEchinococcusSensuVeterinary medicineFecesEchinococcosisBiologyQuarter (Canadian coin)GenusGeographyMedicineZoologyEcologyArchaeology

Abstract

fetched live from OpenAlex

Abstract A total of eight foxhound packs in England and Wales were screened for Echinococcus species using a genus-specific coproantigen ELISA and for Echinococcus granulosus sensu lato and Echinococcus equinus by coproPCR. Main screening ( n = 364 hounds) occurred during 2010–2011 wherein a quarter (25.6%) of the foxhound fecal samples tested were Echinococcus coproantigen-positive (93/364). In total, five of eight (62.5%) hunts screened had coproantigen-positive hounds; coproantigen prevalence for individual foxhound packs ranged from 0 to 61.2% and was shown to be >30% in three hunts (in counties of Powys, Wales and Northumberland, England). Foxhound fecal samples from six of the eight tested hunts (four Welsh and two English hunts) were positive by coproPCR for E. granulosus s.l (including one sequence confirmation of E. granulosus sensu stricto) and E. equinus DNA. Analysis of hunt questionnaire data suggested that there was an association between poor foxhound husbandry, especially feeding practices and Echinococcus coproantigen prevalence. Clearer guidelines regarding the risk of canine echinococcosis are required for safe management of foxhound hunts in England and Wales.

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 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 score1.000

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 integrity0.0000.000
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 head0.077
GPT teacher head0.418
Teacher spread0.340 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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