Long-Term Visual and Refractive Outcomes following Surface Ablation Techniques in a Large Population for Myopia Correction
Bibliographic record
Abstract
PURPOSE: To evaluate the visual and refractive outcome for four wavefront-guided surface ablation (WGSA) techniques (LASEK, LASEK flap-off [LASEK FO], Epi-LASIK, and Epi-LASIK flap-off [Epi-LASIK FO]) in a large myopic population. METHODS: This retrospective review included 1000 myopic eyes (spherical equivalent [SE] -1.0 to -8.0 diopters [D]) treated with WGSA (VISX STAR S4 with IR) using four different epithelial management techniques. Flaps were either retained (163 Epi-LASIK, 361 LASEK) or discarded (277 Epi-LASIK FO, 199 LASEK FO). Eyes in each group were stratified to either low, mild, moderate, or high myopia based on preoperative SE. Uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), manifest refraction spherical equivalent (MRSE), predictability, lines lost, and haze were compared at 3, 6, and 12 months. RESULTS: At 1 year, UDVA and CDVA of ≥20/20 and 20/15 were comparable across the four procedure groups and within each subgroup of myopia. Predictability was less than or equal to ±0.5 D of intended correction in 96% to 99% of eyes. LASEK FO and Epi-LASIK FO outperformed the EPI-LASIK in achieved MRSE, especially in the high myopia category (-0.012, 0.040, and -0.27 D, respectively, P < 0.05). No eyes lost more than one line of CDVA; and 50% to 60% of eyes in each group gained one or more lines. No significant haze was recorded in any group. There was no statistically significant difference between groups in the preoperative MRSE and efficacy indices except for LASEK FO. CONCLUSIONS: At 1 year, there was no statistically significant difference in visual outcomes between techniques for any degree of myopia. However, the MRSE achieved with LASEK FO and Epi-LASIK FO were closer to emmetropia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".