Cofactors associated with Sudden Acquired Retinal Degeneration Syndrome: 151 dogs within a reference population
Bibliographic record
Abstract
OBJECTIVE: To determine factors associated with sudden acquired retinal degeneration syndrome (SARDS) diagnosed within one referral population. ANIMALS STUDIED: 151 dogs diagnosed with SARDS. PROCEDURES: Breed, age, sex, and body weight were compared between dogs with electroretinogram-confirmed SARDS and dogs presented to the UC Davis Veterinary Medical Teaching Hospital (UCD-VMTH) from 1991 to 2014. RESULTS: SARDS was diagnosed in 151 dogs, representing 1.3% of dogs presented to the UCD-VMTH for ophthalmic disease. Although dogs of 36 breeds were affected, the Dachshund (n = 31, 21%), Schnauzer (16, 11%), Pug (11, 7%), and Brittany (5, 3%) were significantly overrepresented, and the Labrador Retriever (3, 2%) was significantly underrepresented vs. the reference population (P < 0.001). Median (range) age and body weight of affected vs. reference dogs were 8.9 (3-20) vs. 6.8 (0.1-26) years and 12.4 (2.8-52.7) vs. 22.3 (0.1-60) kg, respectively. Dogs 6-10 years of age and between 10-20 kg in body weight were significantly overrepresented in the SARDS population, while dogs <6 years of age were significantly underrepresented (P < 0.01). Spayed females (59% of affected dogs) were significantly overrepresented compared to the reference population, whereas intact females (1% of affected dogs) were significantly underrepresented. CONCLUSIONS: Consistent with previous studies, smaller, middle-aged, spayed female dogs may be at increased risk of developing SARDS. Unlike previous studies, this is the first study comparing a variety of SARDS-affected breeds to a reference population. Potentially increased risk of SARDS in several breeds, particularly Dachshunds, suggests a familial factor that warrants further investigation using genetic techniques.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".