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Record W3011479395 · doi:10.20546/ijcmas.2020.901.167

Clinical Studies on Ear Infections, Microbiological Evaluation and Therapeutic Management in Canines

2020· article· en· W3011479395 on OpenAlexaboutno aff
Jiten Parmar, Neha Rao, Avnee Shah, DB Sadhu, B. B. Bhanderi, DS Patel

Bibliographic record

VenueInternational Journal of Current Microbiology and Applied Sciences · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsnot available
Fundersnot available
KeywordsAntibioticsIncidence (geometry)MedicineOtitisBreedCeftriaxoneAntibiotic sensitivityVeterinary medicineMicrobiological cultureInternal medicineSurgeryBiologyMicrobiologyBacteriaAnimal science

Abstract

fetched live from OpenAlex

A total 116 dogs were presented with various affections of the ear, among them 67 (57.76%) cases were having the symptoms of otitis. The higher incidence was found in the age groups of 1-5 year (44.77%, n=30) followed other age groups. The breed wise incidence of was found to be highest in the Labrador (35.82%, n=24) followed by other breeds. 35 cases had bilateral (52.23%), 21 dogs had only right side ear (31.34%) involvement and 11 dogs had left side ear (16.41%) infection. Out of total 67 cases 58.20 percent (n=39) cases were male and 41.80% percent (n=28) cases were female.102 pus samples were collected aseptically for microbiological evaluation and antibiotics sensitivity test revealed 251 isolates from eight bacterial species. The bacterial isolates had different sensitivity pattern. Staphylococcus Spp. was found to be most dominant isolate and found highly sensitive to Cefotaxim and Ceftriaxone. The antibiotics sensitivity testing revealed Gentamycin (17.93%, n=45) to be highly sensitive drug followed by other antibiotics. All the dogs were recovered well without reoccurrence during period of study.

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)0.002
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 score0.362
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.138
GPT teacher head0.429
Teacher spread0.291 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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