Predicting the Effects of Customized Corneal Cross-Linking on Corneal Geometry.
Bibliographic record
Abstract
Purpose To validate an existing finite element model (FEM) for predicting the flattening effect of corneal cross-linking (CXL) in a clinical scenario and to use this model to investigate the parameters that most influence CXL-induced flattening effects.Methods Retrospective data were collected from two clinical cohorts, each with 20 patients receiving either standard or customized CXL. Data were collected before surgery and at the six-month follow-up. Both CXL treatments were simulated with a FEM calibrated on experimental data. Standard anterior corneal geometry indexes (e.g., sphere, cylinder), as well as the curvature changes observed at follow-up were compared to those predicted by FEM simulations.Results At follow-up, patients who underwent customized CXL exhibited more corneal flattening compared to those who received standard CXL (Kmax-t: -2.28 ± 1.4 D vs. -0.81 ± 1.5 D; P < 0.001). The FEM-predicted curvature reduction in the central CXL regions showed a significant correlation with the follow-up data for both standard (R2 = 0.48, P < 0.01) and customized CXL (R2 = 0.59, P < 0.01). Compared to follow-up data, standard CXL model showed concordance correlation coefficients > 0.9 for nine corneal geometry parameters and customized CXL model for three. Sensitivity analysis demonstrated that a 3 mm Hg increase in intraocular pressure (IOP) combined with a 10% weaker keratoconus region alters flattening outcomes by up to 20%.Conclusions Customized CXL induces a flattening of about 2 diopters in the cone region six months after surgery. The model adequately captured the curvature corrections induced by the treatment in the keratoconus cone region, but showed reduced accuracy in predicting global corneal metrics, particularly for customized CXL. The induced flattening effects depend on the IOP, keratoconus-induced biomechanical weakening, and the fluence delivered to the cone.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".