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Record W4318443324 · doi:10.20546/ijcmas.2022.1110.028

Diagnosis and Incidence of Canine Parvovirus Gastroenteritis

2022· article· en· W4318443324 on OpenAlexaboutno aff
Hemali Anilbhai Patel, Neha Rao, Bharatbhai B. Bhanderi, Shima Saiyad, K. K. Hadiya, Nidhi Patel

Bibliographic record

VenueInternational Journal of Current Microbiology and Applied Sciences · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsCanine parvovirusBeagleIncidence (geometry)Veterinary medicineBreedMedicineLabrador RetrieverParvovirusVaccinationBiologyAnimal scienceInternal medicineVirologyVirusSurgery

Abstract

fetched live from OpenAlex

The present study conducted to carry out incidence of canine parvovirus gastroenteritis at Veterinary Clinical Complex, College of Veterinary Science and Animal Husbandry, Kamdhenu University, Anand, Gujarat, India. A total of 1540 dogs were presented at the Veterinary Clinical Complex, Anand during the study period from October 2021 to February 2022.Faecal samples were for confirmation of canine parvovirus gastroenteritis using the polymerase chain reaction (PCR). Out of 1540 dogs, 145 (9.42%) dogs were found positive for Canine Parvo Virus (CPV) by PCR. The age-wise incidence was the highest 66.20 % (96/145) in 0-3 months followed by 22.06 % (32/145) in 4-6 months, 8.96% (13/145) in 7-12 months and 2.76 % (4/145) in >12 months of age. The breed-wise incidence was the highest 51.03% (74/145) in non-descript breeds followed by Labrador 18.62% (27/15), German shepherd 8.96% (13/145), Doberman7.59% (11/145), Pomeranian 5.52% (8/145), Rottweiler 2.76% (4/145), Pug 2.76% (4/145), and Golden Retriever, Lhasa Apso, Beagle and Mongrel 0.86% (1/145) each. The sex-wise incidence was higher 62.07% (90/145) in males as compared to females 37.93% (55/145). The vaccination status wise incidence was the highest 80.69% (117/145) in unvaccinated dogs followed by vaccinated dogs 17.24% (25/145) and partially vaccinated dogs 2.07% (3/145).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.208

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 head0.023
GPT teacher head0.325
Teacher spread0.302 · 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 designBench or experimental
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

Citations2
Published2022
Admission routes1
Has abstractyes

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