Epidemiology of canine glaucoma presented to University of Zurich from 1995 to 2009. Part 1: Congenital and primary glaucoma (4 and 123 cases)
Bibliographic record
Abstract
OBJECTIVE: To investigate the epidemiology of canine congenital and primary glaucoma in the cases presented to the University of Zurich, Vetsuisse Faculty (UZH) from 1995 to 2009. METHODS: Information was obtained from the computer database of patients examined by members of the UZH Ophthalmology Service, between January 1995 and August 2009. Congenital and primary glaucoma was diagnosed based on the age of onset, the lack of evidence of any antecedent eye conditions, and/or the presence and severity of iridocorneal angle defects. The data was evaluated for breed, gender and age at presentation. RESULTS: A total of 5984 dogs presented to the UZH Ophthalmology service between 1995 and 2009. Four dogs of different breed were diagnosed with congenital glaucoma and 123 dogs were diagnosed with primary glaucoma. For the primary glaucomas the overall male to female ratio (M:F) was 1:1.41 and the age of onset ranged from 0.12 to 18.3 years with a mean of 7.3 ± 3.6 years. Data suggested a predisposition for primary glaucoma in the Siberian Husky, Magyar Vizsla and Newfoundland from 2004 to 2009. CONCLUSION: The report presents the epidemiology of canine congenital and primary glaucomas presented to the UZH from 1995 to 2009. A previous suspicion of predisposition for primary glaucoma in the Newfoundland dog (n = 6) and the Magyar Vizsla breed (n = 8) was confirmed.
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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".