Early Impact of Laser Vision Correction (LVC) on the Stability and Quality of the Retinal Image
Bibliographic record
Abstract
This retrospective comparative study analyzes the early postoperative impact of laser vision correction for myopia on the optical quality and stability of functional vision using a double-pass aberrometer. Retinal image quality and visual function stability were assessed preoperatively, one and three months after myopic laser in situ keratomileuses (LASIK) and photorefractive keratectomy (PRK) using double-pass aberrometry (HD Analyzer, Visiometrics S.L, Terrassa, Spain). The parameters analyzed included vision break-up time (VBUT), objective scattering index (OSI), modulation transfer function (MTF), and Strehl ratio (SR). The study included 141 eyes of 141 patients, of whom 89 underwent PRK and 52 underwent LASIK. No statistically significant differences were noted between the two techniques in any analyzed parameters at three months postoperatively. However, a significant drop was observed in all parameters one month after PRK. Only the OSI and VBUT remained significantly altered from baseline at the three months follow-up visit, with an increased OSI by 0.14 +/− 0.36 (p < 0.01) and a shortened VBUT by 0.57 +/− 2.3 s (p < 0.01). No correlation was found between the changes in optical and visual quality parameters and age, ablation depth, or postoperative spherical equivalent. The stability and quality of the retinal images were similar between LASIK and PRK at three months postoperatively. However, significant degradation in all parameters was found one month after PRK.
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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 | 0.000 |
| 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".