The Study of Emerging Trends of Hospital Occurrence of Surgical Ophthalmic Diseases in Dogs
Bibliographic record
Abstract
Aim: To study the emerging trends of hospital occurrence of the surgical ophthalmic diseases in dogs. Place and Duration of Study: Department of Veterinary Surgery and Radiology, College of Veterinary Science, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, India during the period from October 2020 to March 2021. Methodology: The study was conducted on the dogs presented to the hospital clinics for primary ocular ailments with the objective of reporting the various ocular affections observed in dogs. Results: A total of 141 dogs were studied over a period of six months with the younger dogs most commonly presented for ocular affections than the adult and senile dogs. The incidence of ocular disorders was more in male animals compared to the female animals. Pugs were the most frequently presented breed of dogs for ocular affections followed by Labrador Retriever and Spitz. Bilateral affections were more common than the affections of either right or left eye and cornea was the most commonly affected anatomical structure of the eye followed by the lens and other structures of eye. Conclusion: Pigmentary keratitis, cataract, traumatic proptosis and corneal ulcers were the most common ocular affections observed in more than 50 percent of the study population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".