Femtosecond LASIK Combined With Astigmatic Keratotomy for the Correction of Refractive Errors After Penetrating Keratoplasty
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: To evaluate the outcomes of femtosecond laser in situ keratomileusis (LASIK) compared to combined LASIK and astigmatic keratotomy in the treatment of refractive errors following penetrating keratoplasty. PATIENTS AND METHODS: A retrospective review was performed on 18 eyes of 16 patients who underwent LASIK for visual rehabilitation after penetrating keratoplasty. Seven eyes (38.8%) had undergone paired relaxing incisions with topographic guidance prior to LASIK performance. RESULTS: Preoperative uncorrected visual acuity was 20/100 or worse in 13 eyes (72.2%) and best-corrected visual acuity (BCVA) was 20/40 or better in 15 eyes (83.3%). After LASIK, uncorrected visual acuity was 20/40 or better in 10 eyes (55.5%) and BCVA was 20/40 or better in 17 eyes (94.4%). Three eyes (16.6%) had a loss of 1 to 2 lines of BCVA. No difference in visual outcomes was noted in eyes undergoing LASIK and astigmatic keratotomy versus LASIK alone. An increased complication rate was noted in patients who also underwent astigmatic keratotomy and was associated with flap creation. CONCLUSION: Femtosecond LASIK is effective in reducing ametropia after penetrating keratoplasty. Astigmatic keratotomy might complicate flap creation in LASIK; therefore, photorefractive keratectomy should be considered for patients who had previous astigmatic keratotomy to reduce astigmatism.
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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 | 0.000 | 0.002 |
| 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".