A case of anesthesia mumps in a cat captured with magnetic resonance imaging.
Bibliographic record
Abstract
A 6-year-old neutered male domestic shorthair cat was presented because of a 4-week history of upper respiratory noise and suspected upper respiratory tract infection that were unresponsive to antibiotics. On physical examination, the cat had multiple cranial nerve deficits involving cranial nerves III, V, VII, and potentially VIII. Magnetic resonance imaging (MRI) of the head under general anesthesia was conducted. During the MRI, before intravenous contrast administration, the mandibular and parotid salivary glands became acutely symmetrically enlarged. Subsequent cytology of the salivary gland showed no cytological abnormalities. A condition called "anesthesia mumps" has been reported in humans, in which the salivary glands become acutely enlarged during or following general anesthesia. This is a transient swelling, and the underlying cause is unknown; however, several mechanisms have been proposed in the human literature, including physical obstruction of the salivary duct due to patient positioning, administration of anticholinergic drugs, dehydration, and other causes of salivary stasis. A suspected case of anesthesia mumps was reported in a dog following an elective neutering. This is the first reported case of anesthesia mumps in a cat. More importantly, it is the only case in which the acuteness of the swelling was captured with MRI. Key clinical message: A case of acute transient salivary gland swelling secondary to general anesthesia is described. Anesthesiologists and other veterinary professionals should be aware of this rare and benign but potentially alarming anesthesia complication.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| 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".