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Record W3098655688 · doi:10.20546/ijcmas.2020.909.137

Clinical Epidemiology of Fractures in Dogs: A Retrospective Study

2020· article· en· W3098655688 on OpenAlexaboutno aff
P. M. Usadadiya, Reena Bhatt, A.R. Bhadaniya, V. D. Dodia, K. S. Gameti

Bibliographic record

VenueInternational Journal of Current Microbiology and Applied Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineUlnaBreedHumerusIncidence (geometry)EpidemiologyFemurTibiaFibulaRetrospective cohort studyForelimbSurgeryVeterinary medicineInternal medicineAnatomyBiologyAnimal science

Abstract

fetched live from OpenAlex

The present study was carried out to determine the clinical epidemiology of bone fracture in dogs based on three years data of Veterinary Clinical Complex, College of Veterinary Science & A. H., Junagadh Agricultural University, Junagadh, Gujarat. During study, signalment and clinical details of three years (April, 2017 – March, 2020) cases were analyzed that revealed a total number of 9556 dogs presented for various ailments, in which 2289 (23.95%) cases were of surgical affections. Out of these, 275 (12.01%) cases were of orthopaedic affections. During the study period, higher incidence of fracture was seen in femur followed by tibia-fibula, radius-ulna and humerus. Dogs in the age group of 1-6 months showed highest occurrence in comparison to other age group. Breed wise incidences of fracture was higher in non-descript breed followed by German shepherd, Labrador, Spitz and others while sex wise male was more affected then female. In this study, incidence of right hindlimb fracture was found higher followed by fracture of left hindlimb, right forelimb and left forelimb.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.186

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 head0.089
GPT teacher head0.449
Teacher spread0.360 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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