Early Experience With Implantable Collamer Lens in the Management of Hyperopia After Radial Keratotomy
Bibliographic record
Abstract
PURPOSE: To report the initial experience of the use of implantable collamer lens (ICL) in the management of hyperopia after radial keratotomy (RK). METHODS: Single-center, retrospective chart review. Four eyes of 3 patients with secondary hyperopic shift after myopic RK had a mean spherical equivalent of 5.31 D (range, 3.25-9 D) on presentation. All of them underwent ICL implantation to correct the refractive error. RESULTS: There were no intraoperative complications. At a mean follow-up of 5.5 months (range, 3-7 months), the mean uncorrected visual acuity improved from 20/130 preoperatively to 20/24 postoperatively, and the mean spherical equivalent decreased from 5.31 D preoperatively to 0.08 D postoperatively. At 1-month follow-up, all eyes had an uncorrected visual acuity better than or equal to preoperative best spectacle-corrected visual acuity. Two eyes were within 0.25 D and all were within 0.5 D of the predicted refractive target. CONCLUSIONS: ICL implantation is an effective surgical option to consider in the management of hyperopia after RK. However, a large cohort and longer follow-up are needed to determine the long-term efficacy and safety of this procedure in this clinical setting.
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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".