Juvenile Cellulitis in a Labrador retriever- A case report
Bibliographic record
Abstract
A labrador retriever pup aged 50 days was brought to Madras Veterinary College Small animal outpatient Unit with a history of anorexia, pyrexia, dullness with papules and pustules over muzzle and ear pinnae since three days. Facial edema, periocular edema and enlargement of pre scapular and submandibular lymphnodes were noticed on clinical examination. After complete physical examination, blood samples, skin scrapings, samples for bacterial culture and cytology were taken. Hemogram revealed anemia, serum biochemistry revealed low serum total protein concentration, skin scrapings were negative for parasites, no growth was present on bacterial culture from aspirated pustules, cytology from moist lesions showed presence of neutrophils and cocci. Based on the physical examination, hematological, biochemical and microbiological investigations, the present case was diagnosed as Canine Juvenile cellulitis and treatment initiated with antibiotic and corticosteroid parenterally for three days. There was a remarkable improvement noticed with reduced facial edema, reduced periocular edema and dry external ear pinnae. Treatment was continued with oral antibiotic for seven days and the pup recovered uneventfully.
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 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 | 0.001 |
| 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".