Wavefront-Guided Photorefractive Keratectomy in the Treatment of High Astigmatism Following Keratoplasty
Bibliographic record
Abstract
PURPOSE: To report the outcome of wavefront-guided photorefractive keratectomy (WG-PRK) in the treatment of high astigmatism following keratoplasty. METHODS: A retrospective, interventional analysis including patients with high astigmatism following either penetrating keratoplasty or deep anterior lamellar keratoplasty, who underwent WG-PRK. RESULTS: Thirteen eyes (7 right eyes) of 12 patients (10 male) aged 35.1 ± 5.9 years were included. Preoperative astigmatism ranged between 3.00 and 5.00 D. Average follow-up time was 14.0 ± 6.2 months. Uncorrected distance visual acuity (UDVA) improved from 0.97 ± 0.58 logarithm of the minimum angle of resolution (logMAR) (Snellen equivalent ∼20/187) preoperatively to 0.13 ± 0.15 logMAR (Snellen equivalent ∼20/27) at 6 months and 0.14 ± 0.16 logMAR (Snellen equivalent ∼20/28) at the final follow-up (P = 0.001 and P = 0.002, respectively). UDVA ≥20/40 increased from 1 eye (7.7%) preoperatively to 13 eyes (100%) at 6 months and 12 eyes (92.3%) at the final follow-up (P < 0.001 for both). UDVA ≥20/25 increased from 1 eye (7.7%) preoperatively to 6 eyes (46.2%) at 6 months and at the final follow-up (P = 0.027 for both). Mean astigmatism improved from -3.98 ± 0.75 D to -1.27 ± 0.82 D and -1.40 ± 1.04 at 6 months and at the last follow-up, respectively (P = 0.001 for both). Preoperative astigmatism was ≥3.00 D in all eyes and was reduced to ≤2.50 D in all eyes at 6 months postoperatively, with 7 eyes (63.6%) having ≤1.00 D of astigmatism at both 6 months and the final follow-up. CONCLUSIONS: WG-PRK was safe and effective in the treatment of high and regular postkeratoplasty astigmatism.
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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) | 0.000 | 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".