Distribution of corneal spherical aberration in a comprehensive ophthalmology practice and whether keratometry can predict aberration values
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it