Epidemiology of canine glaucoma presented to University of Zurich from 1995 to 2009. Part 2: secondary glaucoma (217 cases)
Bibliographic record
Abstract
OBJECTIVE: To investigate the epidemiology of canine secondary glaucomas in the cases presented to the University of Zurich, Vetsuisse Faculty (UZH) from 1995 to 2009 focusing on possible risk factors for developing secondary glaucoma in this population of dogs. METHODS: Information was obtained from the computer database of patients examined by members of the UZH Ophthalmology Service, between January 1995 and August 2009. Secondary glaucoma was diagnosed based on the presence of antecedent eye conditions. The data was evaluated for breed, gender, age at presentation, and for antecedent eye conditions known to cause glaucoma including anterior uveitis of unknown cause (AU), lens luxation (LL), intraocular surgery (SX), intraocular neoplasia (IN), unspecified trauma to the globe (T), ocular melanosis (OM), hypermature cataract (PY), hyphema (HY), and six other less frequent conditions. RESULTS: A total of 217 dogs were diagnosed with secondary glaucoma from 1995 to 2009. The age of the dogs with secondary glaucoma ranged between 88 days and 19 years (mean 7.7 ± 3.6 years). Data suggested a predisposition for secondary glaucoma in the Cairn Terrier and the Jack Russell Terrier breeds from 2004 to 2009. Common causes of secondary glaucoma from 1995 to 2009 were AU (23.0%), LL (22.6%), SX (13.4%), IN (10.6%), T (8.3%), OM and PY (both 6.9%) and HY (3.23%). CONCLUSION: The report presents the epidemiology of secondary glaucomas presented to UZH from 1995 to 2009. Fourteen risk factors were recorded for secondary glaucoma. This is the first paper documenting OM in the Swiss Cairn Terrier dog population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".