Laser-assisted subepithelial keratectomy in children
Bibliographic record
Abstract
PURPOSE: To evaluate whether laser-assisted subepithelial keratectomy (LASEK) achieves effective targeted myopic correction with less post-treatment corneal haze than observed with photorefractive keratectomy (PRK) in children who fail traditional forms of treatment for myopic anisometropic amblyopia and high myopia. SETTING: Nonhospital surgical facility with follow-up in a hospital clinic setting. METHODS: This prospective study comprised 36 eyes of 25 patients. The mean patient age at treatment was 8.27 years (range 1.0 to 17.4 years). Patients were divided into 3 groups: those with myopic anisometropic amblyopia (13 patients/13 eyes), those with bilateral high myopia (11 patients/22 eyes), and those with high myopia post-penetrating keratoplasty (1 patient/1 eye). All patients were treated with LASEK under general anesthesia using the Visx 20/20 B excimer laser and a multizone, multipass ablation technique. Although the myopia was as high as -22.00 diopters (D) spherical equivalent (SE) in some eyes, no eye was treated for more than -19.00 D SE. RESULTS: At 1 year, the mean SE decreased from -8.03 D to -1.19 D. Forty-four percent of eyes were within +/-1.0 D of the targeted correction; 78% of eyes had clear corneas with no haze. In the entire group, the mean best corrected visual acuity improved from 20/80 to 20/50. A functional-vision survey demonstrated a positive effect on the patients' ability to function in their environments after LASEK. CONCLUSIONS: Laser-assisted subepithelial keratectomy in children represents another method of providing long-term resolution of bilateral high myopia and myopic anisometropic amblyopia with minimal post-laser haze. The reduction in post-laser haze with LASEK compared to that with the standard PRK technique may represent an advantage in treating these complex patients.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".