Quantifying refractive error in companion dogs with and without nuclear sclerosis: 229 eyes from 118 dogs
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship between nuclear sclerosis (NS) and refractive error in companion dogs. ANIMALS STUDIED: One hundred and eighteen companion dogs. PROCEDURES: Dogs were examined and found to be free of significant ocular abnormalities aside from NS. NS was graded from 0 (absent) to 3 (severe) using a scale developed by the investigators. Manual refraction was performed. The effect of NS grade on refractive error was measured using a linear mixed effects analysis adjusted for age. The proportion of eyes with >1.5 D myopia in each NS grade was evaluated using a chi-square test. Visual impairment score (VIS) was obtained for a subset of dogs and compared against age, refractive error, and NS grade. RESULTS: Age was strongly correlated with NS grade (p < .0001). Age-adjusted analysis of NS grade relative to refraction showed a mild but not statistically significant increase in myopia with increasing NS grade, with eyes with grade 3 NS averaging 0.58-0.88 D greater myopia than eyes without NS. However, the myopia of >1.5 D was documented in 4/58 (6.9%) eyes with grade 0 NS, 12/91 (13.2%) eyes with grade 1 NS, 13/57 (22.8%) eyes with grade 2 NS, and 7/23 (30.4%) eyes with grade 3 NS. Risk of myopia >1.5 D was significantly associated with increasing NS grade (p = .02). VIS was associated weakly with refractive error, moderately with age, and significantly with NS grade. CONCLUSIONS: NS is associated with visual deficits in some dogs but is only weakly associated with myopia. More work is needed to characterize vision in aging dogs.
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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 | 0.001 | 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".