Wavefront-Guided Photorefractive Keratectomy in the Treatment of High Astigmatism Following Keratoplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it