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Estudo epidemiológico retrospectivo de cães portadores de ruptura do ligamento cruzado cranial: 323 casos (1999 a 2005)

2007· article· pt· W1780710923 on OpenAlexaboutno aff
Júlia Maria Matera, Angélica Cecília Tatarunas, Renata Moris Domenico Oliveira, Milena Brugnaro, Renata Ferri Macchione

Bibliographic record

VenueBrazilian Journal of Veterinary Research and Animal Science · 2007
Typearticle
Languagept
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

O objetivo do presente trabalho foi caracterizar a população de cães portadores de ruptura do ligamento cruzado cranial (RLCC) para fatores considerados de risco como raça, idade, sexo e peso corporal. A base de dados consistiu de informações resgatadas de prontuários de 323 cães com diagnóstico de ruptura de ligamento cruzado cranial atendidos no Serviço de Cirurgia de Pequenos Animais do Hospital Veterinário da Faculdade de Medicina Veterinária e Zootecnia da Universidade de São Paulo em um período de 7 anos (1999 a 2005). Os resultados mostraram que as raças de maior prevalência foram o Leonberger (100%), Cane Corso (66,7%), Dogue de Bordeau (50%), Starffordshire Terrier (40%) e o Chow Chow (36%), estando ainda o Rottweiler (11,6%) e o Labrador Retriever (8,1%) em 10º e 13º lugares, respectivamente. As idades de maior freqüência foram 2, 4, 3, 7 e 6 anos (média = 5,58 anos). Fêmeas (59,14%) foram mais acometidas do que machos (40,86%) e animais inteiros (76,15%) foram mais representativos do que animais castrados (17,76%). Quanto ao peso, entre 6 e 15 kg (32,82%) seguido por animais entre 36 e 45 kg (17,03%). Pode-se observar que os dados em nosso meio tendem a se assemelhar com a literatura estrangeira: ocorrência da RLCC em animais mais jovens de raças de grande porte e gigantes, o Rottweiler como uma raça em comum a fêmea é mais representativa do que o macho.

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)0.031
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.003
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 head0.122
GPT teacher head0.436
Teacher spread0.314 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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