Photorefractive Keratectomy With 0.02% Mitomycin C for Treatment of Residual Refractive Errors After LASIK
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy and safety of prophylactic mitomycin C (MMC) during photorefractive keratectomy (PRK) over LASIK flaps for the treatment of residual refractive errors following LASIK. METHODS: In this single center, retrospective clinical study, 30 eyes of 33 patients (mean age 37.2 years) who had MMC (0.02%, 30 to 120 seconds) during PRK for the treatment of residual refractive errors following myopic LASIK were evaluated. The retreatment procedures were performed with a VISX S4 laser with iris registration. All patients underwent slit-lamp microscopy, manifest and cycloplegic refraction, corneal topography, pachymetry, pupillometry, and wavefront analysis pre- and postoperatively. All patients underwent follow-up at 1 day, 1 week, and 1, 3, and 6 months and thereafter as required. RESULTS: Mean time between LASIK and PRK retreatment was 67.3 months (range: 7 to 113 months). No intra- or postoperative complications occurred during primary LASIK or PRK retreatment. Mean spherical equivalent refraction of attempted correction with PRK was -0.94 diopters (D) (range: -2.38 to +0.75 D). At mean 7.1-month follow-up, the average uncorrected visual acuity (UCVA) improved from 20/50 (range: 20/30 to 20/200) to 20/28 (range: 20/15 to 20/70). Twenty-seven of 30 eyes showed improvement in UCVA. Two eyes had subjective improvement of glare symptoms (and objective improvement in higher order aberrations), and one eye lost one line of best spectacle-corrected visual acuity due to unrelated corneal abrasion in the postoperative period. None of the eyes in the cohort developed postoperative haze. CONCLUSIONS: Photorefractive keratectomy with prophylactic MMC (0.02%) is a safe and effective option for treating myopic regression following LASIK. A single intraoperative application of 0.02% MMC for as few as 30 seconds was effective in preventing postoperative haze formation.
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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 | 0.000 | 0.001 |
| 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".