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Occurrence of cutaneous candidiasis among domestic dogs in Thrissur

2024· article· en· W4406467237 on OpenAlexaboutno aff
Abitha J. Soman, K. Justin Davis, P.V. Tresamol, K. Vijayakumar, Binsy Mathew

Bibliographic record

VenueJournal of Veterinary and Animal Sciences · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsnot available
Fundersnot available
KeywordsDermatologyMedicineVeterinary medicineBiology

Abstract

fetched live from OpenAlex

The present study was aimed to determine the occurrence of cutaneous candidiasis in dogs. Thirty dogs confirmed positive for Candida spp., by cultural, biochemical and molecular techniques. The occurrence was higher in dogs within the age group of 6 months to 2 years (43%) followed by adult dogs 2 to 6 years (30%), senile dogs more than 6 years (17%) and puppies less than 6 months (10%). The sex predisposition of cutaneous candidiasis did not show any significant differences. The occurrence was higher in Labrador retriever dogs (47%) followed by Dachshund (13%), Pug (10%), Beagle, Shih Tzu and crossbred dogs (7% each). The disease was observed more often in dogs with mixed diet practice (57%) and indoor housing (67%). The predominant clinical signs observed were dermatitis, otitis, intertrigo, paronychia and perineal candidiasis. The lesions were distributed mainly in the ear (54%), skin folds (17%), and combined form (29%). In summary, this research sheds light on the risk factors for cutaneous candidiasis in dogs, highlighting the age groups, breeds, and environmental factors that may influence the occurrence of the condition. The findings provide valuable insights for veterinarians and pet owners, aiding in the understanding and management of cutaneous candidiasis in canines. Keywords: Canine, cutaneous candidiasis, occurrence

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.336
Teacher spread0.299 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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