TREATMENT OF URINARY VESICLE CALCULI IN A FEMALE POMERANIAN DOG
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".