Long-term Results of Corneal Cross-linking for Recurrence of Keratoconus After Keratoplasty
Bibliographic record
Abstract
Purpose: To evaluate the long-term efficacy of accelerated corneal cross-linking (A-CXL) in halting the progression of recurrence of keratoconus in the graft. Methods: This was a retrospective chart review of patients who underwent A-CXL for recurrence of keratoconus between January 2017 and December 2018. Results of eye evaluations performed up to 4 years after A-CXL included slit-lamp examination, corneal tomography and biomechanics, binocular corrected distance visual acuity (CDVA), and the manifest refraction equivalent sphere. The efficacy of A-CXL was assessed with reference to thinnest corneal thickness (TCT), mean anterior axial radius curvature in corneal periphery (ARC), and posterior axial radius curvature (PRC) tomography data indicating the stability of the ectasia. Results: Data from 25 eyes were collected. Significant worsening in TCT, ARC, PCR, maximum keratometry, posterior eccentricity, and CDVA ( P = .01) were found before A-CXL. At 4 years of follow-up, no changes in all tomographic parameters and significant improvement in CDVA ( P = .02) and in corneal biomechanics (stiffness parameter A1, deformation amplitude ratio highest, inverse concave radius, and appla-nation 2 velocity; P = .01) were found. None of the included eyes developed postoperative complications or required re-grafting or refractive procedures. Conclusions: A-CXL is a safe procedure that could play a role in preventing graft ectatic changes in patients with recurrence of keratoconus after keratoplasty, strengthening the graft and halting the natural progression of the ectasia, with positive effects in improving CDVA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".