Nasopharyngeal Vascular Hamartoma in a Dog
Bibliographic record
Abstract
An 8-year-old spayed female 32 kg Labrador retriever was presented for further investigation into the underlying cause of dyspnea, stertor, and sleep apnea present for three months and worsening over 30 days. There were significant reduction in airflow through the nares and loud inspiratory stridor. Thoracic and cervical radiographs made were normal. A skull CT and retrograde rhinoscopy showed a mass occluding the majority of the nasopharynx above the caudal third of the hard palate. The main differential diagnoses included a neoplastic mass vs. inflammatory mass vs. cyst vs. mucous obstruction. There was no destruction of nasal turbinates, making a benign etiology more likely. Biopsy of the mass showed an inflammatory process. En bloc excision of the mass was performed via ventral rhinotomy without complication. Histopathology of the excised mass revealed it to be a mucosal vascular hamartoma. The dog recovered uneventfully and had no further respiratory issues, short or long term. Although vascular hamartomas are a rare finding in veterinary medicine, they can be found in a wide variety of species and anatomic locations. They should be considered when naming differentials for benign mass lesions throughout the body, including the nasopharynx. Although they are benign masses in nature, they can be clinically significant and should be addressed. Prognosis after removal in this location is excellent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".