Ascites in a Labrador retriever puppy with Babesiosis – A case report
Bibliographic record
Abstract
A 2-month-old male Labrador puppy was presented to Referral Veterinary Polyclinic-Teaching Veterinary Clinical Complex, ICAR- Indian Veterinary Research Institute with a history of inappetence and bilaterally distended abdomen for a period of 15 days. On physical examination, generalized weakness, pale mucous membrane, pyrexia, tachycardia, tachypnea and fluid thrill on the abdomen were noticed. Severe anemia, reduced hematocrit, neutrophilic leukocytosis, hypoproteinemia, hypoalbuminemia and elevated liver enzymes were noticed in haemato-biochemical examination. Babesia sp.was detected in blood smear examination. Ultrasound examination revealed hepatomegaly and ascites. Based on these findings, the case was diagnosed as ascites due to Babesiosis. Forty ml of whole blood was transfused and the animal was treated with Inj. Dextrose, Inj. Imidocarb, Inj. Furosemide for 2 days. Case was discharged with the advice of continuing the treatment with triple drug therapy (doxycycline, clindamycin and metronidazole) for 10 days, hepato-protectant (Susp. Silybon®), haematinic (Syp. Hemobest®) and diuretics (Spironolactone and furosemide combination) orally for 30 days at home and regular follow up. The animal showed uneventful recovery following treatment.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".