The Genetic Effect on Refractive Error and Anterior Corneal Aberration: Twin Eye Study
Bibliographic record
Abstract
PURPOSE: To investigate the role of heredity in determining refractive variables, anterior corneal curvature, and anterior corneal aberrations. METHODS: Thirty-three monozygotic and 10 dizygotic twin pairs were enrolled in this study. Corneal curvature, corneal astigmatism, and corneal topography were obtained from computerized videokeratoscope. The CTView program was used to compute anterior corneal aberrations from corneal height data of the videokeratoscope. Correlation analysis was performed to investigate the symmetry of the refractive error, corneal curvature, corneal astigmatism, and anterior corneal aberrations between right and left eyes of each twin pair. Heritability (h2) of these parameters was also calculated. RESULTS: Positive correlations were noted between right and left eyes for spherical power, total astigmatism, mean corneal curvature, and corneal astigmatism. In monozygotic twins, vertical coma, secondary vertical coma, spherical aberration, and secondary spherical aberration were moderately correlated. In dizygotic twins, vertical coma, secondary horizontal coma, and spherical aberration were moderately correlated. In unrelated controls, secondary vertical coma, secondary horizontal coma, and secondary spherical aberration were moderately correlated. Root-mean-square (RMS) of higher order aberrations (3rd to 6th orders), RMS of spherical aberration, and RMS of coma were moderately correlated between right and left eyes in all three groups. Heritability of spherical aberration, RMS of spherical aberration, and corneal astigmatism (h2 = 0.56, 0.44, and 0.46) were greater than those of refractive power, corneal curvature, and other higher order aberrations. CONCLUSIONS: These results suggest that corneal astigmatism and spherical aberration possess a greater genetic predisposition than those of other refractive errors and higher order aberrations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".