Surgical correction of third eyelid gland prolapsed (Cherry eye) in dog - First case report in Bangladesh
Bibliographic record
Abstract
Nictitans Gland Prolapse (NGP), also known as third eyelid gland prolapse or Cherry eye disease, is a serious and common surgical affection of eyes of dogs. Although numerous surgical procedures have been documented, more studies are required to find out the most efficient method. The objective of the present case study is to investigate the outcome of surgical correction of Cherry eye in a dog. In the study a castrated male Labrador Retriever dog of 2.5 years old, weighing 26 kg was presented to the Shahedul Alam Quadary Teaching Veterinary Hospital, Chattogram Veterinary and Animal Sciences University, Chattogram. The patient came with a history of swollen mass at medial canthus of the right eye since 3 months. Clinical examination revealed swollen mass, congested blood vessels, frequent blinking of eyelid and epiphora in right eye. According to the clinical history and examination, the present case was diagnosed as third eyelid gland prolapse (Cherry eye) and was decided for surgical correction by Morgan’s Pocket technique. Surgery was performed by xylazine and ketamine anaesthesia. After the successful surgery and proper postoperative care for 2 weeks, the patient was fully recovered. No reoccurrence or complication was noticed upto 6 months of post operation. The authors suggest that the Morgan’s Pocket technique can be applied for the Cherry eye correction 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".