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Epidemiology of urolithiasis in dogs from Guadalajara City, Mexico

2019· article· en· W2946716155 on OpenAlexaboutno aff
Claudia Iveth Mendóza-López, Javier del Ángel Caraza, María Alejandra Aké-Chiñas, Israel Alejandro Quijano-Hernández, Marco Antonio Barbosa-Mireles

Bibliographic record

VenueVeterinaria México OA · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsEpidemiologyStruviteBreedCalcium oxalateMedicineVeterinary medicineAnimal scienceUrinary systemInternal medicineBiology

Abstract

fetched live from OpenAlex

Urolithiasis is a frequent and recurrent problem in dogs around the world. Several epidemiological studies based on mineral composition of uroliths have been carried out in different geographical areas. The objective of this study was to analyze epidemiological data of 195 dogs with urolithiasis from the metropolitan area of Guadalajara Jalisco, Mexico. To determine the chemical composition of uroliths, quantitative and qualitative analyses were performed by means of stereoscopic microscopy and infrared spectroscopy. The dogs` median age was six years and a male-female ratio of 1.4:1 was observed. The most affected pure breed dogs were schnauzer, poodle, Labrador retriever, Yorkshire terrier, and German shepherd. The frequency of uroliths of struvite, calcium oxalate, urates, mixes, and compounds, is similar to the one found in other studies performed in other populations. However, a much higher frequency of silicate-containing uroliths (16.92%) was observed, both in a pure form as well as in mineral mixtures. These results led us to suggest the need to develop further investigations to determine the origin of this high frequency.Figure 1. Different types of uroliths. Note the differences in shape, size, color, and number; however, none of these characteristics is specific to a particular mineral composition.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.118
GPT teacher head0.354
Teacher spread0.236 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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