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Record W2805652494 · doi:10.1055/s-0038-1641132

Isolated Avulsion of the Tendon of Insertion of the Infraspinatus and Supraspinatus Muscles in Five Juvenile Labrador Retrievers

2018· article· en· W2805652494 on OpenAlexaboutno aff
Alessandro Piras, Laura Hakala, Karoliina Mikola

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2018
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAvulsionJuvenileTendonAnatomyBiology

Abstract

fetched live from OpenAlex

CASE HISTORY: Five juvenile Labrador Retrievers between the ages of 6 and 8 months were presented to our referral centres with a history of intermittent forelimb lameness. CLINICAL EXAMINATION: The clinical examination revealed the presence of bilateral orthopaedic problems in four out of five cases. DIAGNOSTIC IMAGING FINDINGS: Radiographic and computed tomography examinations showed the presence of a radiolucent defect corresponding to the area of insertion of the infraspinatus or supraspinatus tendons on the proximal humerus. Three dogs were concurrently affected by elbow disease on the contralateral forelimb and one dog with bilateral infraspinatus avulsion also had osteochondritis dissecans affecting both shoulder joints. DIAGNOSIS: Avulsion of the insertion of the infraspinatus tendon in four dogs and of the supraspinatus tendon in one dog. CLINICAL RELEVANCE: According to the current literature, the incidence of infraspinatus and supraspinatus tendinopathies in adult Labrador Retrievers is higher than in other breeds. In our five cases, the patients were juvenile and the nature of the injury was an avulsion of the tendinous insertion. Avulsion of the tendon of insertion of the infraspinatus or supraspinatus has been poorly described in the veterinary literature, and this would represent the first series of cases affecting juvenile Labrador Retrievers.

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)0.000
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 score0.347
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.060
GPT teacher head0.306
Teacher spread0.246 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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