Topography-Guided Photorefractive Keratectomy for Postkeratoplasty Astigmatism: Long-Term Outcomes
Bibliographic record
Abstract
PURPOSE: To evaluate the long-term efficacy and safety of topography-guided photorefractive keratectomy (TG-PRK) for postkeratoplasty refractive error correction. METHODS: A retrospective interventional case series of 54 eyes of 50 patients who underwent previous corneal transplants. Unaided distance visual acuity (UDVA) and best corrected visual acuity (CDVA), manifest refraction, mean central keratometric value, mean keratometric astigmatism, and postoperative complications were reviewed. RESULTS: Final follow-up was at mean 31 (±17) months. Sixteen point seven percent of eyes underwent more than 1 surface ablation. Mean UDVA improved from 0.96 ± 0.06 logarithm of the minimum angle of resolution (LogMAR) preoperatively to 0.46 ± 0.05 LogMAR of resolution at the final follow-up (Bonferroni, P < 0.0001). Mean UDVA improved by 4.4 Snellen lines. Improvement in CDVA was not significant, although a significant improvement was noted when eyes with preoperative CDVA <20/40 were analyzed separately (t test, P = 0.005). Mean astigmatism improved from -4.4 ± 0.26 D preoperatively to -2.4 ± 0.26 D at the final follow-up (Bonferroni, P < 0.0001), whereas mean SEQ improved from -2.5 ± 0.39 D preoperatively to -1.1 ± 0.25 D (Bonferroni, P = 0.02). In total, 9% at the preoperative visit and 55% at the final visit had less than 2 D of astigmatism, respectively. Keratometric astigmatism decreased from 5.24 ± 0.36 D preoperatively to 2.98 ± 0.34 D at the final follow-up (t test, P < 0.0001). No eyes developed clinically significant haze, 14.8% developed regression, and 13% had a reduction of 2 or more CDVA lines. CONCLUSIONS: Postkeratoplasty topography-guided photorefractive keratectomy has good long-term efficacy and safety, resulting in significant UDVA, refractive, and keratometric improvement. Regression can occur after the first year of treatment, emphasizing the importance of long-term follow-up.
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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 | 0.000 | 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".