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TREATMENT OF URINARY VESICLE CALCULI IN A FEMALE POMERANIAN DOG

2025· article· W4417101438 on OpenAlexaboutno aff
Ni Made Wida Rieke Pitaloka, I Gusti Agung Gde Putra Pemayun, I Gusti Ngurah Sudisma

Bibliographic record

VenueBuletin Veteriner Udayana · 2025
Typearticle
Language
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsUrinary systemUrineIntravenous pyelographyLabrador RetrieverUltrasoundXylazineAntibiotics

Abstract

fetched live from OpenAlex

Calculi of the urinary vesica is one of the many problems that can occur in pets, especially dogs. This case study aims to find out how to diagnose and treat a case of urinary vesica calculi in a female dog. A female pomeranian dog named Monna, aged 2.4 years with a body weight of 2.7 kg had complaints of difficulty urinating, straining when urinating, and blood in the urine which lasted for approximately 3 months. Supporting examinations were carried out in the form of an ultrasound examination with the discovery of a hyperechoic mass formation in the vesica urinaria suspected of calculi and radiographic examination found a radiopaque image in the vesica urinaria which was believed to be calculi. Based on the results of the supporting examination, the dog was diagnosed with calculi in the vesica urinaria with a prognosis of fausta. The dog was treated with laparocystotomy surgery using a combination of xylazine and ketamine anesthesia intravenously. The calculi found in the case dog showed characteristics of rough, sharp and jagged edges, irregular round shape, with a hard and strong composition, where the calculi found in this case were calculi formed from calcium oxalate. Postoperatively the dog was given Cefotaxime antibiotics (20 mg/kg BW, q12, IV) for 3 days and continued with Cefixime antibiotics (10 mg/kg BW, q12, PO) for 7 days. On the 10th postoperative day, the surgical wound had dried and fused perfectly. The dog had normal activities, normal defecation and urination.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.339
Teacher spread0.291 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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