Two-Staged Sequential Management of Post-LASIK Ectasia: Under-Flap Corneal Cross-Linking for Stabilization Followed by Flap Surface Topography-Guided PRK for Visual Optimization
Bibliographic record
Abstract
Background/Objectives: To evaluate the efficacy, accuracy, safety, and long-term stability of topography-guided photorefractive keratectomy (TGPRK) in eyes where post-LASIK (PLE) ectasia progression was stabilized with under-flap corneal crosslinking (ufCXL). Methods: This retrospective interventional case series included six eyes from five patients with PLE after microkeratome LASIK. All eyes underwent ufCXL to halt ectatic progression. A shallow TGPRK enhancement was performed on the LASIK flap surface after corneal and refractive stability was confirmed (18 months median) post ufCXL Outcome measures included uncorrected and corrected distance visual acuity (UDVA, CDVA), spherical equivalent (SEQ), refractive astigmatism, keratometry, and corneal irregularity indices over a mean follow-up of 47 months. Results: ufCXL stabilized ectatic progression but left residual refractive errors, limiting UDVA. TGPRK performed subsequently significantly improved UDVA, from 0.38 to 0.10 LogMAR (p = 0.017), and increased the LASIK efficacy index from 0.46 to 0.83 (p = 0.0087). Refractive astigmatism was reduced in all eyes achieving a SEQ within ±1.00 D of the target. Long-term stability was maintained, with no ectasia progression, no change in SEQ, no change in corneal irregularity indices, and no increase in maximal keratometry. Conclusions: TGPRK performed in ufCXL stabilized corneas can safely correct residual refractive errors, resulting in significant and sustained improvements in both refractive and visual outcomes in PLE.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".