Quantifying Age-Related Anterior and Posterior Corneal Astigmatism Relationships: Insights for IOL Calculators and Topography-Guided LASIK Protocols
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 investigate the age-related interactions between anterior corneal astigmatism (ACA) and posterior corneal astigmatism (PCA) and their impact on total corneal astigmatism (TCA) using a high-resolution topographer in a large cohort of patients of all ages. Methods A retrospective review of 19,317 eyes seeking refractive surgery was conducted. ACA, PCA, and TCA were measured using the Pentacam HR (Oculus Optikgeräte GmbH). Corneal data were categorized into astigmatism axes subgroups: with-the-rule (WTR), oblique, and against-the-rule (ATR), and analyzed across 10 age groups. ACA, PCA, and TCA (magnitudes and axes), along with Pearson correlation coefficients between ACA and PCA, were calculated for all groups. Correlations between “TCA minus ACA” and selected variables were also assessed. Results ACA decreased by 37.6% from 18 to 65 years old, followed by a 9.8% increase after 72 years ( P < .001). From 18 to 87 years, PCA consistently decreased by 33.3% with age ( P < .001). TCA showed a 28% decrease from ages 18 to 59 years, followed by a 32.3% increase after 72 years ( P < .001), which we termed a “V-shaped” trend. In WTR ACA eyes, the correlation between ACA and PCA decreased from R = 0.78 at 18 years to R ⩽ 0.48 in eyes 65 years or older. Oblique ACA eyes showed lower correlations, peaking at R = 0.51 at 24 years and decreasing to R = 0.02 after 72 years. ATR ACA eyes showed a mild positive correlation in midlife ( R = 0.15; 41 years), switching to a moderate inverse correlation in older age ( R = −0.3461; ⩾ 72 years). Higher ACA magnitude, WTR ACA axis, and young age showed strong likelihood of overcorrection if ignoring PCA in laser vision correction (LVC), intraocular lens (IOL), or phakic IOL (PIOL) refractive treatment ( P < .0001), whereas lower ACA magnitude, ATR ACA axis, and older age were contrarily more likely to undercorrect if ignoring PCA ( P < .0001). Conclusions ACA versus PCA correlations are strongly age-dependent across all orientations. These findings demonstrate that incorporating age-specific correlations into modern IOL calculators could improve TCA prediction accuracy, thereby improving results in refractive IOL surgery. [ J Refract Surg . 2025;41(6):e520–e531.]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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