Refractive surgery for myopic astigmatism: systematic review for assessing efficacy, stability, and complication
Bibliographic record
Abstract
Astigmatism is a common refractive error characterized by meridional asymmetry in corneal or lenticular curvature, often coexisting with myopia. It significantly impacts visual function and quality of life. Refractive surgery offers a promising solution, with several techniques available, including LASIK, small incision lenticule extraction (SMILE), and phakic intraocular lens (pIOL) implantation. This systematic review aims to evaluate the efficacy, stability, and safety of various refractive surgeries specifically for myopic astigmatism. A literature search was conducted across PubMed, Embase, Cochrane Library, and Google Scholar. Risk of Bias-2 and the Newcastle-Ottawa Scale were used for quality assessment. Seven studies met inclusion criteria: four randomized controlled trials and three retrospective analyses. Outcomes included changes in manifest refractive astigmatism, spherical equivalent, visual acuity, postoperative stability, and complications. Wavefront-guided LASIK showed superior correction of astigmatism and spherical equivalent compared to SMILE in several studies. However, other comparisons found FS-LASIK and SMILE to offer similar results. All techniques improved visual acuity, with LASIK demonstrating slightly better outcomes in some cases. SMILE, while less effective in higher astigmatism due to limited cyclotorsion control, had fewer flap-related issues and a lower risk of dry eye. Toric pIOLs showed better astigmatism correction than non-toric variants with limbal relaxing incisions, which remained a cost-effective alternative. All procedures showed stable postoperative results and acceptable safety profiles. Most complications were minor and specific to the surgical technique. In conclusion, wavefront-guided LASIK offers the most consistent refractive correction for myopic astigmatism. However, SMILE and pIOLs are viable options depending on individual patient factors. Personalized preoperative evaluation remains key, and further high-quality randomized trials are warranted to refine treatment strategies.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".