Refractive, visual, and subjective quality of vision outcomes for very high myopia LASIK from − 10.00 to − 13.50 diopters
Bibliographic record
Abstract
BACKGROUND: To evaluate laser-assisted in situ keratomileusis (LASIK) outcomes, subjective quality of vision (QoV) and patient satisfaction in eyes with very high myopia (VHM) above - 10.00 diopters (D). METHODS: Consecutive myopic and myopic-astigmatism eyes with spherical equivalent (SEQ) ranging between - 10.00 to - 13.50 D underwent LASIK with the WaveLight® Allegretto Wave® Eye-Q 400 Hz excimer laser. Treatment accuracy, efficacy, safety, stability, cylinder vectors, and higher-order aberrations were evaluated, together with subjective QoV and night vision disturbances (NVDs). RESULTS: = 0.71). The efficacy index was 0.93 ± 0.20, with 51 and 81% of eyes achieving 20/20 and 20/25. The astigmatism correction index was 0.95 ± 0.33. The safety index was 1.05 ± 0.12. The average myopic regression was - 0.51 ± 0.38 D. Preoperative QoV scores improved significantly postoperatively (7.5 ± 0.8 vs. 9.1 ± 0.7; P < 0.001), with less NVDs (P < 0.001). Total, spherical and coma root mean square (RMS) postoperative ocular higher-order aberrations were 1.07 ± 0.34, 0.67 ± 0.25, and 0.70 ± 0.40 μm. CONCLUSIONS: Very high myopia LASIK between - 10.00 to - 13.50 D is safe and results in good visual outcomes, with high patient satisfaction and a significant improvement in patient-reported QoV after surgery. Appropriately selected patients within this very high myopia group can be included as LASIK candidates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".