Comparison of manual and femtosecond astigmatic keratotomy in the treatment of postkeratoplasty astigmatism
Bibliographic record
Abstract
PURPOSE: To compare the outcomes of femtosecond astigmatic keratotomy (FSAK) and manual astigmatic keratotomy (AK) in treatment of postkeratoplasty astigmatism. METHODS: A retrospective, comparative, pairwise-matched case series including 150 patients who underwent either FSAK (n = 75) or manual AK (n = 75) for the treatment of astigmatism (>3.00 D) following penetrating keratoplasty or deep anterior lamellar keratoplasty. Pairwise matching for baseline variables (age, visual acuity and astigmatism) was performed. RESULTS: Mean age was 57.5 ± 16.0 years. The FSAK group had significantly better postoperative best-corrected visual acuity (BCVA) (p = 0.010), uncorrected visual acuity (UCVA) (p = 0.049), corneal astigmatism (p = 0.020) and manifest astigmatism (p < 0.001) compared with the manual AK group. Gain of ≥3 lines in BCVA (logMAR) was seen in five eyes (6.7%) and 21 eyes (28.0%) in manual AK and FSAK, respectively (p = 0.005). Alpins vector analysis showed lower (closer to 0) index of success (0.50 ± 0.24 and 0.79 ± 0.48, p < 0.001) and higher (closer to 1) correction index (0.94 ± 0.45 and 0.74 ± 0.55, p = 0.020) in FSAK compared with manual AK. Corneal and manifest astigmatism improved significantly in both groups, while BCVA and UCVA improved significantly in FSAK only. Repeat AK rate was 32% (24 eyes) in manual AK and 4% (three eyes) in FSAK (p < 0.001). Overcorrection-related re-suturing rate was 0% in manual AK and 8% (six eyes) in FSAK (p = 0.037). There was one microperforation (1.3%) in FSAK, and there were no occurrences of graft dehiscence, infectious keratitis or graft rejection. CONCLUSIONS: Both manual AK and FSAK were safe and effective in reducing postkeratoplasty astigmatism. FSAK had superior visual and keratometric outcomes compared with manual AK.
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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 | 0.001 | 0.002 |
| 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".