Median lingual hair heterotopia associated with pyogranulomatous glossitis in a Labrador retriever: Surgical treatment using carbon-dioxide laser.
Bibliographic record
Abstract
A 9-year-old male Labrador retriever dog was presented with dysphagia and presence of hairs on the tongue. Buccal examination revealed ulcerative glossitis and lingual hairs along the midline. Ultrasound and magnetic resonance imaging of the tongue showed multiple hair shafts contained in a proliferative tissue along the midline and extending in a fistulous tract towards the right ventral aspect of the tongue at mid-length. Surgical excision was completed using a carbon-dioxide laser. Histopathological examination revealed a pyogranulomatous inflammation centered on growing hairs, confirming the diagnosis of glossitis and lingual hair heterotopia. At 10 mo after surgery, all clinical signs and glossitis had disappeared despite partial recurrence of hair on the dorsal sulcus and in the sublingual fistula. Key clinical message: Although lingual hair heterotopia usually has no clinical repercussions, associated ulcerative lesions should support imaging and biopsy.Resection of the lesion using a carbon-dioxide laser resulted in a good outcome in this case, but recurrent hair growth is possible.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".