Mitomycin C in Photorefractive Keratectomy
Bibliographic record
Abstract
PURPOSE: To evaluate the outcome of primary or retreatment photorefractive keratectomy (PRK) or phototherapeutic keratectomy (PTK) with mitomycin C (MMC) 0.02% applied prophylactically intraoperatively for the prevention of haze and regression in cases of significant haze and regression after primary PRK, due to previous radial keratotomy (RK), in primary PRK eyes with high myopia, and in comeas with a previous superficial foreign body scar. METHODS: This was a retrospective evaluation of 34 eyes treated with PRK or PTK and intraoperative MMC. Mitomycin C was applied immediately after laser ablation. Postoperative examinations were conducted 3 and 7 days after surgery and monthly for 6 months. Haze was graded on a standard 0+ (clear cornea) to 4+ (total opacity) scale. Visual acuity was measured as a general baseline indicator. RESULTS: Postoperatively, uncorrected visual acuity (UCVA) was 20/20 or better in 21 eyes and 20/25 in 5 eyes with best spectacle-corrected visual acuity (BSCVA) of 20/20 or better. Three eyes achieved UCVA of 20/30 with BSCVA of 20/20 or better; and 5 eyes had UCVA of 20/40 with BSCVA of 20/20 or better. Nineteen (56%) eyes had grade 0+ haze and 15 (44%) eyes had grade 0.5+ haze (trace haze) at 6-month follow-up. CONCLUSIONS: Mitomycin C 0.02% used prophylactically during PRK or PTK retreatment was effective in preventing significant recurrent haze from developing. In eyes with high myopia or previous RK or scarring, MMC was effective in preventing significant 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 | 0.002 |
| 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".