Factors Influencing Astigmatic Correction Using Small‐Incision Lenticule Extraction: A Systematic Review and Meta‐Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose To systematically review SMILE‐based astigmatism correction and influencing factors. Methods Literature was screened across eight databases. Pre‐ and post‐SMILE cylinder, difference vector (DV), correction index (CI), magnitude of error (ME), angle of error (AE), and index of success (IOS) were compared. Bias was assessed using Cochrane’s Risk of Bias, Quality Assessment of Diagnostic Accuracy Studies, and the Newcastle–Ottawa Scale. Results Elevated ocular residual astigmatism (ORA) resulted in greater postoperative residual astigmatism, accompanied by increased DV and IOS ( p < 0.05), whereas ME, AE, and CI remained unaffected by ORA levels. Postoperative cylinder, DV, ME, AE, CI, and IOS were comparable between eyes ( p > 0.05). Correction outcomes were impacted by ocular rotation, astigmatism characteristics, spherical degree, corneal curvature, and patient age. Conclusions SMILE effectively corrects low, moderate, and high astigmatism, but high ORA patients tend to experience undercorrection. But accuracy requires vector planning.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it