Corneal biomechanical properties after SMILE versus FLEX, LASIK, LASEK, or PRK: a systematic review and meta-analysis
Bibliographic record
Abstract
The aim of this study was to compare the postoperative corneal biomechanical properties between small incision lenticule extraction (SMILE) and other corneal refractive surgeries. A systematic review and meta-analysis were conducted. Articles from January 2005, to April 2019, were identified searching PubMed, EMBASE, Web of Science, and International Clinical Trials Registry Platform. Studies that compared SMILE with other corneal refractive surgeries on adult myopia patients and evaluated corneal biomechanics were included. Multiple effect sizes in each study were combined. Random-effects model was conducted in the meta-analysis. Twenty-two studies were included: 5 randomized controlled trials (RCTs), 9 prospective and 6 retrospective cohort studies, and 2 cross-sectional studies. Using the combined effect of corneal hysteresis (CH) and corneal resistance factor (CRF), which were obtained from ocular response analyzer (ORA), the pooled Hedges’ g of SMILE versus femtosecond laser-assisted in situ keratomileusis (FS-LASIK) was 0.41 (95% CI, 0.00 to 0.81; p = 0.049; I 2 = 78%), versus LASIK was 1.31 (95% CI, 0.54 to 2.08; p < 0.001; I 2 = 77%), versus femtosecond lenticule extraction (FLEX) was − 0.01 (95% CI, − 0.31 to 0.30; p = 0.972; I 2 = 20%), and versus the group of photorefractive keratectomy (PRK) and laser-assisted sub-epithelial keratectomy (LASEK) was − 0.26 (95% CI, − 0.67 to 0.16; p = 0.230; I 2 = 54%). The summary score of Corvis ST (CST) after SMILE was comparable to FS-LASIK/LASIK with the pooled Hedges’ g = − 0.05 (95% CI, − 0.24 to 0.14; p = 0.612, I 2 = 55%). In terms of preserving corneal biomechanical strength after surgeries, SMILE was superior to either FS-LASIK or LASIK, while comparable to FLEX or PRK/LASEK group based on the results from ORA. More studies are needed to apply CST on evaluating corneal biomechanics after refractive surgeries.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".