Management of Closed Pyometra-induced Acute Kidney Injury by Intermitent Hemodialysis in a Labrador Retriever Dog
Bibliographic record
Abstract
Aims: This case report underscores the importance of intermittent hemodialysis (IHD) for treatment of closed pyometra induced AKI in dog. Presentation of Case: A 9-year-old female Labrador retriever dog with history of inappetence, lethargy, intermittent vomiting, melena, polyuria and polydipsia was presented to Multispecialty Hospital, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana. Initial physical assessment revealed body condition score- 3, congested mucus membrane, with normal vital parameters. Complete blood counts revealed neutrophilic leukocytosis and severe left shift. Biochemistry revealed derailed renal function values [Blood urea nitrogen (BUN)- 93 mg/dl, Creatinine- 9.7mg/dl, Sodium (Na)-150 mEq/l, Potassium (K)- 4 mEq/l, Chloride (Cl)- 108 mEq/l, Phosphorus (P)- 18.4mg/dl). Routine urine analysis was normal. Ultrasound examination revealed distended uterine horns with echogenic material, measuring approximately 4.07 cm. However, cortico-medullary differentiation, size and contour of both the kidneys was within the normal limits suggesting pyometra with AKI. Initially to counter the AKI, IHD was undertaken along with rational treatment to extend the window of renal recovery as well as to undertake surgical intervention for ovario-hysterectomy. After surgery, dog was again referred to dialysis unit due to elevated uremic toxins. Blood gas analysis revealed metabolic acidosis with compensatory alkalosis. Again IHD was started immediately to lower down the blood creatinine level. After three sessions of hemodialysis animal’s renal function values (creatinine: 2.1mg/dl) and BUN: 27mg/dl) improved with resolved clinical signs. Discussion and Conclusion: This case report explains the management of complicated cases of pyometra with renal involvement using IHD concomitant with surgical intervention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".