Wavefront-guided Multipoint (Segmental) Custom Ablation Enhancement Using the Nidek NAVEX Platform
Bibliographic record
Abstract
PURPOSE: To present our clinical experience regarding enhancement (retreatment) of previously performed non-wavefront-guided refractive surgery by wavefront-guided multipoint (segmental) custom ablation utilizing the Nidek NAVEX platform. METHODS: Retrospective clinical analysis was conducted of 20 eyes (19 patients) with mixed myopic or hyperopic astigmatism who had undergone primary laser in situ keratomileusis (LASIK) or photorefractive keratectomy (PRK) and reported postoperative reductions in quality of vision. These vision disturbances correlated with clinically significant elevations in the root mean square of higher order aberrations (RMS of HOA) values. Before wavefront-guided multipoint (segmental) custom ablation enhancement with the NAVEX platform, all patients underwent testing with the Nidek Optical Path Difference Scan (OPD-Scan) and analysis with Final Fit Software. RESULTS: Twenty eyes showed improvement or resolution of visual symptoms following wavefront-guided multipoint (segmental) custom ablation enhancement. The postoperative root mean square of higher order aberration values were variable and not always related to improvement in visual function. No patient lost two or more lines of best spectacle-corrected visual acuity. CONCLUSION: Topography and wavefrontguided multipoint (segmental) custom ablation enhancements were safe and effective in improving visual symptoms following primary refractive surgery. In some eyes, improved visual function without correspondingly lower RMS of HOA values may be an effect of neutralizing some chromatic aberrations across the visible light spectrum, thereby improving the modulation transfer function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".