Distribution of corneal spherical aberration in a comprehensive ophthalmology practice and whether keratometry can predict aberration values
Bibliographic record
Abstract
PURPOSE: To determine the spherical aberration of the cornea in the general population and whether keratometry readings are predictive of corneal spherical aberration values. SETTING: Private comprehensive ophthalmology practice. METHODS: Corneal spherical aberration and keratometry readings were measured in 696 normal eyes of patients presenting for ocular examination to a comprehensive ophthalmologist. The Easygraph (Oculus) was used to measure the corneal topography and keratometry readings in patients with healthy corneas. The analysis was performed using software in the Easygraph to determine the Zernike coefficients for each cornea. The keratometry and spherical aberration (Zernike coefficient Z(4)(0)) were then statistically analyzed. RESULTS: The corneal spherical aberration, analyzed by the Kolmogorov-Smirnov test for normality, fit a normal Gaussian distribution. The spherical aberration value was (+0.274 +/- 0.089) x 10(-3), measured at an optical zone of 6.0 mm. A very weak correlation was found between corneal spherical aberration and central keratometry readings of the cornea: Corneal spherical aberration = {0.017 x (mean keratometry) - 0.457} x 10(-3). CONCLUSIONS: The corneal spherical aberration distribution was a normal Gaussian curve. However, the mean value was significantly different when the sex of the patient was considered. Corneal keratometry readings could not be reliably used to predict corneal spherical aberration.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".