Posterior Corneal Astigmatism Does Not Influence Manifest-Treated Topography-guided LASIK Outcomes
Bibliographic record
Abstract
Purpose: To investigate whether the magnitude of posterior corneal astigmatism (PCA) impacts refractive and visual outcomes of primary topography-guided laser in situ keratomileusis (LASIK) and to provide guidance on treating eyes with high PCA. Methods: Comparative retrospective analysis of 4,541 consecutive eyes treated with Contoura (Alcon Laboratories, Inc) on the manifest refractive astigmatism. Standard outcomes of the 1,514 eyes with the lowest PCA (first tercile; low PCA group) were compared to the 1,514 eyes with the highest PCA (last tercile; high PCA group). Pearson correlation coefficient was used to assess relationships between variables. Results: Preoperatively, 20.9% of eyes presented with PCA of 0.50 diopters (D) or greater. The mean PCA was 0.18 ± 0.07 D in eyes with low PCA, and 0.50 ± 0.11 D in eyes with high PCA. An equivalent number of eyes achieved a cumulative postoperative unilateral uncorrected distance visual acuity of 20/20 in both the low PCA and high PCA groups (95.3% vs 94.7%; P = .4489). The efficacy index of both low and high PCA eyes was identical (0.99 ± 0.06 vs 0.99 ± 0.08; P = .3192), as was the safety index (1.00 ± 0.02 vs 1.00 ± 0.03; P = .0110). The magnitude of preoperative PCA was weakly correlated with postoperative refractive astigmatism ( R = 0.1323), but not with postoperative defocus equivalent ( R = −0.0414) or spherical equivalent ( R = −0.0128). Conclusions: PCA does not negatively impact the outcomes of topography-guided LASIK targeting the manifest refraction, having identical accuracy, efficacy, and safety in eyes with both low and high PCA. There is no scientific basis to measure and consider PCA in topography-guided LASIK planning software or nomograms if the excimer laser treatment input targets the manifest refraction. [ J Refract Surg . 2022;38(12):780–790.]
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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 | 0.005 |
| 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".