Effect of litter size and birth weight on naturally occurring myopia in the Labrador retriever
Bibliographic record
Abstract
Abstract Purpose To evaluate early environmental influences (e.g. litter size, birth weight, birth season) on adult refractive error in dogs. A previous familial aggregation analysis has shown that the distribution of refractive error in a large family of pedigree Labrador Retrievers has a significant genetic component, but that litter size and other residual/environmental factors also have significant effects. Methods Refractive error was measured by cycloplegic retinoscopy in both eyes of 166 dogs, 1‐8 years of age and free of ocular pathology, from a large family of pedigree Labrador Retrievers. All dogs originated from the same breeding colony. The early records of these dogs included information on birth weight, maternal litter cohort, litter size and neonatal weight gain, measured daily for the first 6 weeks. These factors were analyzed to investigate their effect on adult refractive error. Results The average adult spherical equivalent refraction (SER) was ‐0.44D (‐5.38D to +1.65D, n = 166): 35% were myopic (SER ≤ ‐0.50D), 58% emmetropic (SER = ‐0.49 to +0.99) and 7% hyperopic (SER ≥ +1.00D). Mean birth weight was 421±57g. Higher birth weight was weakly (R=0.3) correlated with more hyperopic adult refractions. Relative to large litters (≥ 7), dogs from small litters (< 7) gained more weight within the first 6 weeks of life and were on average 0.43D more myopic. Conclusion The dog provides a unique model for studying a wide range of environmental influences on the development of naturally occurring, high prevalence, low degree myopia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".