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Record W2054393994 · doi:10.15381/rivep.v24i1.1659

FRECUENCIA DE PRESENTACIÓN DE INESTABILIDAD LUMBOSACRA EN CANINOS LABRADOR RETRIEVER

2013· article· es· W2054393994 on OpenAlexaboutno aff
Dennis Arana C., Diego Díaz C., Víctor Fernández A., César Gavidia C., Vicente Chilón C.

Bibliographic record

VenueRevista de Investigaciones Veterinarias del Perú · 2013
Typearticle
Languagees
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLumbosacral jointPalpationConcordanceLabrador RetrieverOverweightSurgeryBody mass indexInternal medicine

Abstract

fetched live from OpenAlex

The aim of the study was to determine the frequency of lumbosacral instability in Labrador Retriever dogs older than six months of age, and to determine its association with the variables sex, age, body weight, radiological signs, and clinical signs of lumbosacral pain. The disease was detected by radiographic evaluation. Sixty dogs were randomly selected and distributed according to sex (30 per sex), age (3 groups) and body weight (3 categories). The frequency of lumbosacral instability was 75.0% (45/60). The females were most affected (86.7%, 26/30) and male:female ratio was 1:1.4. None statistical association was found due to age. Obese animals (>15% overweight on the limit of the standard) showed greater frequency (87.5%, 21/24). The most frequent radiological sign was the ventral subluxation of the sacrum relative to L7 (75.0%, 45/60). Transrectal palpation allowed the highest detection of lumbosacral pain (80.0%, 45/60). The radiological evaluation of the disease showed a high degree of concordance with the evaluation of the lumbosacral pain (p<0.05).

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.293
Teacher spread0.263 · 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; both teacher heads agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

Explore more

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