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PREVALENCE AND CLINICAL CHARACTERISTICS OF EHRLICHIA CANIS INFECTION IN DOGS IN THUA THIEN HUE

2022· article· en· W4312822706 on OpenAlexaboutno aff
Vu Van Hai, Nguyẽn Dinh Thùy Khuong

Bibliographic record

VenueHUE UNIVERSITY JOURNAL OF SCIENCE ECONOMICS AND DEVELOPMENT · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsnot available
Fundersnot available
KeywordsEhrlichia canisCanisMedicineLabrador RetrieverVomitingAnorexiaInternal medicinePathologyImmunologySerologyBiologyAntibody

Abstract

fetched live from OpenAlex

The survey was carried out at OKADA PET Veterinary Center, Hue City, with 935 dogs of different ages and breeds. The results show that 39.6% of dogs have the clinical signs of Ehrlichia canis (E. canis) infection, 95.7% of which were serologically positive for E. canis antibody. The results also indicate that 54.9% of dogs have E. canis morulae in monocytes and/or neutrophils. Statistical analysis reveals that the prevalence of E. canis infection in dogs is not affected by breed, gender or age. The clinical symptoms of infected dogs are very complex, including fever, abortion, joint pain, breast tumours, short breathing, nasal haemorrhage, weakness, pale mucosa, skin inflammation, hair loss around the eyes, eye discharges, cloudy eyes, refuging to eat, diarrhoea, belly skin haemorrhage, anorexia, constipation, ascites, vomiting, depression, salivation, and metritis. About 37.9% of dogs are serologically infected with Ehrlichia canis with various symptoms.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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