Refractive results with SMILE using lower energy settings in the United States
Bibliographic record
Abstract
PURPOSE: To report the visual and refractive results of small incision lenticule extraction (SMILE) with low energy settings in the United States (US) and to evaluate outcomes for low astigmatism treatment. SETTING: Private clinical practice. DESIGN: Retrospective cohort study. METHODS: This study retrospectively reviewed 462 consecutive eyes that underwent SMILE with lower energy settings. Inclusion criteria included all patients between the ages of 19-39 with myopic astigmatism up to -11.25 diopters (D) spherical equivalent (sphere up to -10.00 D, astigmatism up to -3.00 D), and corrected distance visual acuity of at least 20/25. Eyes with low astigmatism (0.25 D-0.50 D) were also included. Outcome analysis was performed according to the Standard Graphs for Reporting Refractive Surgery at postoperative month (POM) 1, and POM 3-6 when data were available. RESULTS: The mean preoperative spherical equivalent treated was -4.96 ± 2.07; at POM 1, 92% of eyes achieved uncorrected visual acuity (UCVA) of 20/20 or better and maintained visual stability throughout the remainder of the study. At last visit, 431 eyes (93%) achieved UCVA of 20/20 or better, and 461 eyes (99.8%) were 20/25 or better. Ninety-seven (21%) eyes gained at least 1 Snellen line of corrected distance visual acuity and no eyes lost 2 or more lines. Almost all eyes (n = 453, 98%) were within 0.5D of target; 85% of eyes with low astigmatism had ≤0.25 D at last visit compared to 80% of eyes with moderate astigmatism. CONCLUSIONS: SMILE with U.S.-approved low energy settings is safe, predictable, and efficacious and provides patients with a fast visual recovery.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".