Evaluating the Effectiveness of Selective Breeding and Corrective Surgery on Entropion in Labrador Retrievers From a Guide Dog Program
Bibliographic record
Abstract
ABSTRACT Objective To evaluate the outcome of implementing a selective breeding strategy to reduce the incidence of entropion in a guide dog program. Animals Studied Labrador Retrievers born at Guide Dogs for the Blind. 2106 whelped between July 2013 and October 2016 prior to selective breeding, and 1958 whelped between May 2020 and December 2024 after selective breeding for a total of 4064 puppies. Procedure Retrospective review of medical records. Bayesian logistic regression modeling was used to calculate the heritability of entropion and compare the risk of entropion diagnosis for dogs before and after the implementation of a selective breeding strategy. Results Prior to selective breeding, the incidence of entropion was 6.46%. After selective breeding, the incidence of entropion was 3.12%. Of the 197 puppies diagnosed with entropion (median age at first diagnosis 6.7 weeks), 181 had a recorded surgical repair procedure (median age at first repair 7.9 weeks). Repair was corrective in 164 cases (90.61%) with no recurrence of entropion. Dogs whelped in the pre‐selection cohort had 1.96 times greater risk of entropion diagnosis than dogs whelped in the post‐selection cohort. Heritability of entropion ( h 2 ) in this population is estimated to be 0.80. Conclusion These results provide evidence that in a population of Labrador Retrievers where the heritability of entropion is high, selective breeding can greatly impact the incidence of entropion, and surgical repair is an effective form of treatment at a young age.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".