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Record W2185521118 · doi:10.2460/ajvr.76.12.1085

Effect of castration on the urinary protein-to-creatinine ratio of male dogs

2015· article· en· W2185521118 on OpenAlexaff
Marie-Blanche Bertieri, Catherine Lapointe, Bérénice Conversy, Carolyn Gara‐Boivin

Bibliographic record

VenueAmerican Journal of Veterinary Research · 2015
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsUrinalysisCastrationUrologyMedicineUrineProteinuriaCreatinineUrinary systemDipstickReference rangeEndocrinologyKidney

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the urinary protein-to-creatinine ratio (UPCR) of healthy sexually intact male dogs and to compare the UPCR of these dogs before and after castration. ANIMALS: 19 client- or shelter-owned healthy adult sexually intact male dogs. PROCEDURES: Physical, hematologic, and biochemical examinations and urinalysis (including calculation of the UPCR) were performed on each dog. Dogs were then castrated, and physical examination and urinalysis (including calculation of the UPCR) were performed again at least 15 days after castration. RESULTS: A dipstick test yielded positive results for protein in the urine of 10 sexually intact male dogs, but the UPCR was < 0.5 for all sexually intact male dogs. Mean UPCR for sexually intact male dogs was 0.12 (range, 0.10 to 0.32). The UPCR was < 0.2 for all castrated dogs, except for 1. Mean UPCR for all castrated dogs was 0.08 (range, 0.05 to 0.69). There was a significant difference between mean UPCR before and after castration. CONCLUSIONS AND CLINICAL RELEVANCE: In this study, pathological proteinuria was not detected in sexually intact male dogs. Positive results for a urine dipstick test should be interpreted with caution in sexually intact male dogs and should be confirmed by assessment of the UPCR. An increased UPCR in sexually intact male dogs may be considered abnormal.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.221
GPT teacher head0.461
Teacher spread0.240 · 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 designNon-randomized trial
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

Citations5
Published2015
Admission routes1
Has abstractyes

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