Primary conjunctival mast cell tumor in a Labrador Retriever
Bibliographic record
Abstract
A 4-year-old, intact male Labrador Retriever with a rapidly progressive conjunctival mass was evaluated. Ocular examination showed a 2-cm elongated mass arising from the superior bulbar conjunctiva of the left eye. The mass resulted in distortion of the palpebral fissure and contacted the superior aspect of the cornea without modifying its structure; no adhesion to the sclera was detected. The superior palpebral conjunctiva was unaffected, and the remaining ocular examination was normal. The initial diagnostic work-up included CBC, serum biochemical analysis, urinalysis, and fine needle biopsy of the mass. A poorly differentiated mast cell tumor was diagnosed by cytology. Immunocytochemistry was performed to evaluate Ki-67 proliferation index, and 54/1000 tumoral nuclei showed a dark red staining. After a complete clinical staging, the mass was excised and identified histologically as a grade-II mast cell tumor. An adjuvant treatment with prednisone and vinblastine was instituted because of the limited excisional margins. No evidence of local recurrence or metastasis has been apparent during the 29-month follow-up period. This report contributes to the current literature pertaining to canine conjunctival mast cell tumors; unfortunately, the paucity of case reports and the absence of large studies regarding this tumor make conclusions regarding its biologic behavior impossible.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".