Multiple cranial nerve deficits secondary to a mass lesion of the skull base in a Swiss shepherd dog ( <i>Canis lupus familiaris</i> )
Bibliographic record
Abstract
Abstract A 10‐year‐old, 27 kg, spayed, female Swiss shepherd dog was referred for neurological examination because of a 4‐week history of left eye mydriasis and keratoconjunctivitis sicca, bilateral crusted nasal passages, and progressive left‐sided masticatory muscle atrophy. The neurological examination showed unilateral multiple cranial nerve deficits. Magnetic resonance imaging findings included a mass lesion affecting the sphenoid bones. The intracranial component appeared as an extra‐axial mass primarily affecting the left middle cranial fossa, including the sella turcica, while the extracranial portion caused bilateral obstruction of the nasal passages. This case report highlights the importance of inclusion of skull base mass lesions in the differential diagnosis for multiple cranial nerve deficits in dogs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".