Corneal epithelial inclusion cysts in 12 dogs (13 eyes) from 2010 to 2019: A multicentric retrospective study
Bibliographic record
Abstract
OBJECTIVE: The aim of this retrospective study was to review the clinical data and outcomes of patients that suffered corneal epithelial inclusion cysts (CEIC). Animals studied Thirteen eyes from 12 dogs from multiple private practices in France and Belgium, with a strong clinical of CEIC were included in the study. RESULTS: The mean age of affected dogs was 9 years. There were 9 females and 3 males. Two out of 12 dogs were Shih Tzus. Where identified, etiology was traumatic. Eleven dogs were affected unilaterally, 1 bilaterally. The cysts were single, bi-lobulated, or tri-lobulate, and cyst size ranged from 1 to 6 mm in diameter. Keratectomy alone was performed in seven cases, in conjunction with a conjunctival graft in three cases, a porcine small intestine mucosa graft in two cases, or an amniotic membrane graft in one case. Histopathologic findings were consistent in all eyes with a cyst located in the corneal stroma, composed of a nonkeratinized squamous epithelium delineating a lumen filled with neutrophils and desquamated epithelial cells. Surgery was curative in all cases but one, where the removal was incomplete and recurrence occurred several weeks after the surgery. CONCLUSION: Corneal epithelial inclusion cysts is a rare condition, secondary to corneal trauma, which can be treated successfully with conventional keratectomy alone or in conjunction with graft procedures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".