Reversal of Hepato-renal Impairment Induced by Meloxicam Paracetamol Toxicity in a Labrador Dog
Bibliographic record
Abstract
An 8 years old male Labrador dog was referred to Small animal Medicine Referral Clinic, Veterinary Clinical complex, Veterinary College and Research Institute, Orathanadu for fever, anorexia, hematemesis and melenic stools. The dog on treatment with 2 ml of Meloxicam Paracetamol combination IM, along with Injection Oxytetracycline and fluid therapy for 3 days by locally and did not respond to treatment thus was referred. Clinical dullness, lethargy, respiratory distress, visible mucosal jaundice, ecchymosis on ventral abdomen was noticed. Physical examination revealed, halitosis, ulceration in the tip of the tongue, dehydration, pre scapular LN enlargement, distended abdomen, fluid thrill and splenomegaly. Hematology revealed anemia and thrombocytopenia. Biochemical analysis revealed Hyperbilirubinemia, Elevated BUN, Creatinine, AST, ALT, ALP and hypoalbuminemia. Abdominal ultrasound reveled splenomegaly, focal hyperechoic liver parenchyma, cortex echogenicity of right kidney and perirenal fluid accumulation. ECG revealed peaked T wave. Radiography revealed hepatosplenomegaly. Hemato-biochemical alteration was monitored before and after therapy. Animal was managed with fluid therapy, Acetylcysteine, Amino acid and supportives and the dog had an uneventful recovery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".