Meta-analysis of corneal topography-guided and wavefront aberration-optimized comparison of higher-order aberrations after FS-LASIK
Bibliographic record
Abstract
AIM:To evaluate the effect of higher-order aberrations(HOAs)after topography-guided and wavefront-optimized femtosecond laser-assisted in situ keratomileusis(FS-LASIK). METHODS: We searched on PubMed, the Cochrane Library, Medline, CNKI, CBM, VIP and WanFang Data database for randomized controlled trials(RCTs)and comparative studies(CTs). The published languages were limited to Chinese and English. The risk bias tool provided by the Cochrane cooperation scale and Newcastle-Ottawa Scale were used to assess the risk bias of included studies of RCTs and CTs. The published biases of included studies were assessed by the Egger test. Meta-analysis was performed with Review Manager 5.4.RESULTS: Two randomized controlled trials and six comparative studies with a total of 987 subjects were included(482 in the topography-guided FS-LASIK group, 505 in the wavefront optimized FS-LASIK group). The Meta-analysis showed that the topography-guided group has a better effect than the wavefront-optimized group in spherical equivalent, the difference between the two groups was statistically significant [WMD=0.11, 95%CI (0.07, 0.14), P<0.00001]. And the results also indicated that there was a significant difference between the two groups with HOAs [WMD= -0.09, 95%CI (-0.13,-0.05), P<0.0001], spherical aberrations [WMD=-0.05, 95%CI (-0.09, -0.01), P=0.008] and coma [WMD=-0.08, 95%CI (-0.12, -0.05), P<0.00001].CONCLUSION: Based on the available evidence, topography-guided FS-LASIK has higher diopter and lower HOAs, spherical aberrations and coma than wavefront-optimized FS-LASIK.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.053 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".