<p>Outcomes of Femtosecond Laser Arcuate Incisions in the Treatment of Low Corneal Astigmatism</p>
Bibliographic record
Abstract
PURPOSE: To evaluate real-world outcomes of astigmatism management with femtosecond laser arcuate incisions in patients with low corneal astigmatism (<1.0 D) using a novel formula for arcuate incision calculation compared to outcomes after conventional cataract surgery without surgical management of astigmatism. PATIENTS AND METHODS: The Wörtz-Gupta™ Formula (available at www.lricalc.com) was used to calculate femtosecond laser arcuate parameters for 224 patients with <1 D of corneal astigmatism who underwent cataract surgery; lens power was determined with the Barrett Universal II formula. Uncorrected distance visual acuity (UCDVA) and refractive astigmatism measurements were obtained, with an average follow-up of 4 weeks. RESULTS: The average preoperative cylinder was similar (0.61 D in the femtosecond group [n=124] and 0.57 D in the conventional group [n=100] (P>0.05)). More patients had ≤0.5 D of postoperative corneal astigmatism in the femtosecond group (n=110/124, 89%) than in the conventional group (n=71/100, 71%), respectively (P=0.001). The mean absolute postoperative refractive astigmatism was higher in the conventional surgery group than in the femtosecond group (0.43 ± 0.4 D vs 0.26 ± 0.28 D); these differences were statistically significant (P<0.001). The percentage of patients with UCDVA of 20/20 or better vision was higher in the femtosecond group (62%) than the conventional group (48%) (P=0.025). CONCLUSION: Using the femtosecond laser for arcuate incisions in combination with a novel nomogram can provide excellent anatomic and refractive outcomes in patients with lower levels of preoperative astigmatism at the time of cataract surgery.
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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) | 0.000 | 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".