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 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 | 0.003 | 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) | 0.003 | 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".