Ventricular pneumocephalus, cervical subarachnoid pneumorrhachis, and meningoencephalitis in a dog following rhinotomy for chronic fungal rhinitis
Bibliographic record
Abstract
CASE DESCRIPTION: A 5-year-old 35.8-kg (78.8-lb) neutered male Labrador Retriever was evaluated for chronic nasal discharge associated with a fungal infection. The dog had previously been prescribed antimicrobials and antifungal treatment, but owner compliance was lacking. CLINICAL FINDINGS: Bilateral mucopurulent nasal discharge, mild ulceration of the left nasal commissure, and hyperkeratosis of the dorsal nasal planum were present. Computed tomography revealed destruction of the intranasal structures, focal lysis of the cribriform plate, and invasion of a soft-tissue mass into the frontal cortex. Rhinoscopy revealed a large pale mass in the caudal aspect of the right nasal passage; a biopsy sample was consistent with Aspergillus sp on histologic evaluation. TREATMENT AND OUTCOME: Initial treatment included medical management with an antifungal agent. Approximately 3 months later, a large fungal granuloma in the right frontal sinus was removed and debridement was performed via dorsal rhinotomy. One month after surgery, the dog was evaluated for signs of cervical pain and altered mentation. An MRI and CSF analysis were performed; diagnoses of ventricular pneumocephalus, subarachnoid pneumorrhachis, and meningoencephalitis were made. Management included oxygen therapy and administration of antimicrobials, analgesics, and antifungal medications. On follow-up 9 months after initial evaluation, neurologic deficits were reportedly resolved, and the dog was doing well. CONCLUSIONS AND CLINICAL RELEVANCE: This report emphasizes the importance of prompt, appropriate treatment of fungal rhinitis in dogs. Although rare, pneumocephalus and pneumorrhachis should be included as differential diagnoses for neurologic signs following treatment for this condition. In this dog, the complications were not considered severe and improved over time with supportive care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".