Outcomes of femtosecond laser–assisted cataract and refractive lens surgery in patients with prior radial keratotomy
Bibliographic record
Abstract
PURPOSE: To investigate outcomes of femtosecond laser (FL)-assisted cataract surgery (FLACS) and refractive lens exchange (RLE) in patients with prior radial keratotomy (RK). SETTING: Single clinical practice. DESIGN: Retrospective observational case series. METHODS: All patients with prior RK undergoing FLACS- or FL-assisted RLE surgeries over a 6-year period were reviewed. Inclusion criteria were diurnal stability and stable manifest refraction. Exclusion criteria were any other incisional corneal surgery, macular or glaucomatous pathology, or vision loss due to any other cause. Data collected included demographics, visual acuity, laser settings, and complications. Main outcome measures were intraoperative and postoperative complications and visual outcomes. Safety and efficacy indices were evaluated. RESULTS: 16 eyes of 9 patients were included. Mean age and follow-up time were 59.9 ± 9.9 years (range 44 to 75 years) and 3.3 ± 2.5 months, respectively. The mean number of RK cuts was 11.8 ± 5.3 (range 8 to 20). Mean preoperative uncorrected (UDVA) and corrected distance visual acuity (CDVA) were 0.9 ± 0.4 logMAR (Snellen 20/160) and 0.2 ± 0.3 logMAR (Snellen 20/30), respectively. 2 intraoperative anterior capsule tears were identified. 1 postoperative intraocular lens dislocation occurred. Postoperatively, the mean UDVA and CDVA were 0.2 ± 0.2 logMAR (20/30) and 0.1 ± 0.1 logMAR (20/25), respectively. The safety index was 1.6, and the efficacy index was 1.2. CONCLUSIONS: FLACS- or FL-assisted RLE surgery in RK patients has a high risk for anterior capsule tear and should be avoided. Thickened incisional scars are potential sources of incomplete laser penetrance. Toric lens implantation in RK eyes provide unpredictable astigmatic correction and should also be avoided.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".