Laser-assisted subepithelial keratectomy and photorefractive keratectomy for post-penetrating keratoplasty myopia and astigmatism in adults
Bibliographic record
Abstract
PURPOSE: To evaluate whether laser-assisted subepithelial keratectomy (LASEK) and photorefractive keratectomy (PRK) achieve effective targeted correction and the extent of post-treatment corneal haze after corneal transplantation. SETTING: Nonhospital surgical facility, Calgary, Alberta, Canada. DESIGN: Evidence-based manuscript. METHODS: This study evaluated visual acuity, refractive error correction, and potential complications after LASEK or PRK to eliminate refractive error differences after penetrating keratoplasty in adults. A Nidek EC-5000 or Technolas 217 excimer laser was used in all treatments. RESULTS: At last follow-up (mean 20.50 months post laser), the mean spherical equivalent (SE) decreased from -2.71 diopters (D) ± 4.17 (SD) to -0.54 ± 3.28 D in the LASEK group and from -4.87 ± 3.90 D to -1.82 ± 3.34 D in the PRK group. The mean preoperative uncorrected distance visual acuity (UDVA) was 1.63 ± 0.53 and 1.45 ± 0.64, respectively, and the mean postoperative UDVA, 0.83 ± 0.54 and 0.90 ± 0.55, respectively. The improvement in SE and UDVA was statistically significant in both groups (P < .01). The mean haze (0 to 3 scale) at the last follow-up was 0.46 ± 0.708 in the LASEK group and 0.58 ± 0.776 in the PRK group. CONCLUSIONS: The UDVA improved and refractive errors were effectively reduced after LASEK or PRK in eyes with previous PKP. There was no significant difference in the change in SE, UDVA, or corrected distance visual acuity between LASEK and PRK. Some patients had evidence of corneal haze, although the difference between the groups was not significant.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".