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Record W3173264408 · doi:10.36062/ijah.60.1.2021.66-76

Clinical and neurological evaluation following treatment with ultrasound and diathermy in dogs suffering from posterior paresis

2021· article· en· W3173264408 on OpenAlexaboutno aff
Akshay Tikoo, Nonie Arora, Deepak Kumar Tiwari, Satbir Sharma, Akshay Kumar, Deeapk Kaushik

Bibliographic record

VenueIndian Journal of Animal Health · 2021
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsParesisDiathermyMedicineSurgery

Abstract

fetched live from OpenAlex

The present study was conducted on twelve clinical cases of dogs irrespective of age, breed and sex suffering from posterior paresis.Male dogs were more affected (n=7) than females (n=5).Posterior paresis was observed highest in Mongrel (n=4) followed by Pomeranian and Labrador Retriever (n=3 each) breeds, respectively.Minimum occurrence was shown by German Shepherd and American Bully (n=1 each).Age-wise maximum hospital occurrence was observed in dogs of 1-3 years (n=6), while the minimum was found in less than 1 year and more than 3 years of age (n=3 each).Lateral and ventro-dorsal radiographs of the spine (lumbo-sacral area) were taken in all the animals to evaluate the involvement of vertebral bodies, but no orthopaedic abnormality detected responsible for posterior paresis.Numeric pain score was found to be reduced significantly after treatment in both the groups indicative of improvement in pain and hind limb weakness in dogs with posterior paresis.Further, shortwave diathermy was found to be more effective as compared to therapeutic ultrasound in the management of posterior paresis in dogs.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.111
GPT teacher head0.404
Teacher spread0.293 · 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
Published2021
Admission routes1
Has abstractyes

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