Stepwise Combination of Femtosecond Astigmatic Keratotomy With Phacoemulsification and Toric Intraocular Lens Implantation in Treatment of Very High Postkeratoplasty Astigmatism
Bibliographic record
Abstract
PURPOSE: To report the outcomes of stepwise combined femtosecond astigmatic keratotomy (FSAK) and phacoemulsification with toric intraocular lens (IOL) implantation in the treatment of very high astigmatism after either penetrating keratoplasty or deep anterior lamellar keratoplasty. METHODS: This is a retrospective, interventional case series including 8 eyes of 6 patients with very high astigmatism [≥8.00 diopter (D)] after either penetrating keratoplasty or deep anterior lamellar keratoplasty who underwent FSAK, followed by phacoemulsification and toric IOL implantation. Outcome measures were corneal and manifest astigmatism and uncorrected and best spectacle-corrected visual acuity (UCVA, BSCVA). RESULTS: The average age was 58.9 ± 5.1 years. The average follow-up time was 40.9 ± 43.8 months. Outcome measure changes after both FSAK and toric IOL implantation were: corneal astigmatism improved from 13.56 ± 4.81 D to 4.48 ± 2.83 D (P < 0.001), manifest astigmatism improved from 9.15 ± 3.86 to 1.46 ± 0.88 D (P = 0.011), UCVA improved from 1.69 ± 0.45 LogMAR (Snellen equivalent ∼20/980) to 0.23 ± 0.11 LogMAR (Snellen equivalent ∼20/33, P < 0.001), and BSCVA improved from 1.01 ± 0.71 LogMAR (Snellen equivalent ∼20/200) to 0.19 ± 0.11 LogMAR (Snellen equivalent ∼20/30, P = 0.015). BSCVA and UCVA at the last follow-up were 20/40 or better in all patients. All procedures were uneventful. Two eyes underwent photorefractive keratectomy after FSAK to regularize and further reduce astigmatism before toric IOL implantation. One patient underwent temporary compression suturing because of FSAK overcorrection. CONCLUSIONS: Combined stepwise use of FSAK and phacoemulsification with toric IOL implantation was an effective and apparently safe approach in patients with very high postkeratoplasty astigmatism. Additional treatment using photorefractive keratectomy may be beneficial in some cases.
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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 | 0.001 |
| 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".