Characterization and prevalence of cataracts in Labrador Retrievers in The Netherlands
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence and distribution of types of cataract, investigate the effects of selective breeding on cataract development, and identify the relationship between posterior polar cataract and other types of cortical cataracts in Labrador Retrievers in The Netherlands. ANIMALS: 9,017 Labrador Retrievers. PROCEDURES: Records of 18,283 ophthalmic examinations performed by veterinary ophthalmologists from 1977 through 2005 were reviewed. There were 522 dogs affected by hereditary cataracts in 1 or both eyes without progressive retinal atrophy (PRA) and 166 PRA-affected dogs with cataracts. These cataracts were divided into 3 groups: posterior polar (triangular) cataract, extensive immature and mature cataract, and a miscellaneous group. Dogs with PRA were analyzed separately. RESULTS: From 1980 through 2000, the prevalence of hereditary cataracts was stable at 8%. The prevalence of cataracts in offspring of cataract-affected dogs was significantly increased, compared with the prevalence in offspring of nonaffected dogs. The distribution of types of cataract was significantly different between dogs with primary cataracts and PRA-affected dogs. Dogs with posterior polar (triangular) cataracts produced affected offspring with the same distribution of types of cataracts as the entire population of primary cataract-affected dogs. CONCLUSIONS AND CLINICAL RELEVANCE: Cataract development in the Labrador Retriever population in The Netherlands appears to be a predominantly genetic disorder. Posterior polar (triangular) cataracts appear to be related to other types of hereditary cataract. Although there is no conclusive evidence, it seems valid to continue exclusion of all Labrador Retrievers affected by any type of primary cataract from breeding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".