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Epidemiological study of bacterial dermatitis in dogs of Wayanad district

2022· article· en· W4289824819 on OpenAlexaboutno aff
G. Parvathy Nair, P. M. Deepa, A. Janus, R. Chintu, K. Vijayakumar

Bibliographic record

VenueJournal of Veterinary and Animal Sciences · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsnot available
Fundersnot available
KeywordsVeterinary medicineGram stainingMicrobiological cultureBiologyClinical significanceStaphylococcus aureusMedicineSignificant differenceStaphylococcusMicrobiologyBacteriaPathologyInternal medicineAntibiotics

Abstract

fetched live from OpenAlex

A survey was undertaken to determine the epidemiology of bacterial dermatitis in dogs presented at the Teaching Veterinary Clinical Complex (TVCC), College of Veterinary and Animal sciences Pookode, from April 2017 to June 2018. Dogs of all age groups, breeds and both sexes with clinical signs of dermatitis were included in the study. Sterile swabs were used to collect samples aseptically from the dogs that showed clinical lesions of canine bacterial dermatitis. Samples were taken for culture and isolation of bacteria was done. There was no statistically significant difference among different age groups, however the highest occurrence was observed among dogs between 1-3 years (39.44 %) and out of 71 animals, 39 (54.93 %t) male dogs were positive for bacterial dermatitis, but no statistically significance among different sexes was observed. Among the various breeds studied, the highest occurrence was noticed in Labrador retrievers (23.94 per cent) when compared to other breeds but no statistically significance difference among different breeds was observed. Identification of bacterial isolates was done based on colony character, Gram’s staining, oxidase test, catalase test, oxidative fermentative test and growth in specific media. A total of 71 bacterial isolates were obtained. Bacterial isolates obtained were Staphylococcus species (84.51 %), Streptococci (7.04%), Micrococci (5.63 %), and Pseudomonas species (2.82 %).

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.072
GPT teacher head0.339
Teacher spread0.267 · 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
Published2022
Admission routes1
Has abstractyes

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