Delayed Topographical and Refractive Changes Following Corneal Cross-Linking for Keratoconus
Bibliographic record
Abstract
Background: The aim of this study was to analyze the long-term topographic and refractive outcomes of corneal cross-linking (CXL) in keratoconus. Methods: We used a retrospective observational study of patients with keratoconus who underwent CXL with a minimum follow-up of 5 years. Patients’ refractive and topography data (corrected distance visual acuity, sphere, cylinder, average and maximum keratometry, and corneal aberrations) were collected. Results: A total of 112 patients/150 eyes (mean age: 33.2 ± 10.7 years; range: 13−61) were included. The mean follow-up was 5.87 ± 1.35 years (range: 5−10). At the last follow-up visit, an improvement in CDVA, spherical and cylindrical refraction, average and steepest keratometry, and corneal aberrations were observed (p < 0.05), with the exception of trefoil. At the last visit, 49 (34.8%) and 31 (22.0%) eyes had an improvement beyond 1D in their spherical and cylindrical power, respectively, and 43 (28.7%) eyes had a flattening of their steepest keratometry. Progressive improvement over time was observed for spherical refraction; max and mean-K; as well as corneal RMS, total, high, coma, and spherical aberrations (p < 0.05). More severe disease at the baseline correlated with an improvement in corneal aberrations over time. Conclusions: In addition to a progressive improvement in refractive and keratometric indices, corneal aberrations also demonstrate a steady decline with long-term follow-up after CXL, which was more pronounced in more severe patients.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".