Long-Term Results of Phototherapeutic Keratectomy Versus Mechanical Epithelial Removal Followed by Corneal Collagen Cross-Linking for Keratoconus
Bibliographic record
Abstract
PURPOSE: To compare the long-term visual outcomes of patients with keratoconus treated with either phototherapeutic keratectomy (PTK) or mechanical epithelial removal before corneal collagen cross-linking (CXL) at 1, 3, 6, and 12 months postoperatively. METHODS: CXL was performed by 1 of 3 surgeons (K.B., W.B.J., or G.M.). Seventeen eyes underwent mechanical epithelial removal before CXL and were consecutively selected after being matched with the 17 eyes in the PTK group for the variables of procedure date, average keratometry, and pachymetry. All cones were central. Manifest refraction spherical equivalent, sphere, cylinder, corrected distance visual acuity (CDVA), and pachymetry were measured and compared preoperatively and in follow-up. RESULTS: The mean CDVA change in the PTK group at 12 months postoperatively was statistically different from the mean CDVA change in the mechanical group at 12 months postoperatively (P = 0.031). The PTK group had significantly better outcomes in visual acuity 12 months postoperatively than did the mechanical group (P > 0.05). The mean number of lines of improvement in the PTK and mechanical groups were 2.30 ± 0.96 and 0.00 ± 0.33 lines, respectively (P = 0.0036). The mean change between the preoperative and 12 months postoperative manifest refraction spherical equivalent for the PTK and mechanical groups were 0.78 ± 0.65 and 0.17 ± 0.65, respectively (P > 0.05). CONCLUSIONS: PTK CXL resulted in better visual outcomes in comparison with mechanical epithelial removal CXL 1 year after treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".