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Record W3158468749 · doi:10.1590/1678-4162-12031

Prevalence and risk factors for urinary incontinence in bitches five years after ovariohysterectomy

2021· article· en· W3158468749 on OpenAlexaboutno aff
B. Leupolt, Claudia Barbieri, Leilane Jesus, Álan Gomes Pöppl

Bibliographic record

VenueArquivo Brasileiro de Medicina Veterinária e Zootecnia · 2021
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
FundersUniversidade Federal do Rio Grande do Sul
KeywordsUrinary incontinenceBreedMedicineUrinary systemVeterinary medicinePopulationRisk factorGynecologySurgeryInternal medicineAnimal scienceEnvironmental healthBiology

Abstract

fetched live from OpenAlex

ABSTRACT Ovariohysterectomy (OHE) is the most performed elective surgery in veterinary medicine. Although this procedure brings benefits both to the animal and public health, acquired urinary incontinence is a possible complication resultant from it. The aim of this study was to determine the prevalence of urinary incontinence and evaluate size, breed, and time of surgery as risk factors in a population of spayed female dogs in the Hospital de Clínicas Veterinárias da Universidade Federal do Rio Grande do Sul, in the year of 2013, through the use of a multiple-choice screening instrument. Identified estimated prevalence was 11.27% and main risk factors were as follows: large size (OR = 7.12 IC95% = 1.42 - 35.67), Rottweiler breed (OR = 8.92; IC95% = 5.25 - 15.15), Pit-bull breed (OR = 4.14; IC95% = 2.19 - 7.83), and Labrador breed (OR = 2.73; IC95% = 1.53 - 4.87). Time of surgery was not considered a risk factor for urinary incontinence in this population (OR = 1.45; IC95% = 0.86 - 2.40). Even though most owners reported a small impact on their relationship with the animal, urinary incontinence hazard should be addressed before spaying.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.305
Teacher spread0.265 · 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.

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
Published2021
Admission routes1
Has abstractyes

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