Patient age, refractive index of the corneal stroma, and outcomes of uneventful laser in situ keratomileusis
Bibliographic record
Abstract
PURPOSE: To determine the influence of age and the corneal stromal refractive index on the difference between the predicted and actual postoperative refractive error after laser in situ keratomileusis (LASIK) and whether the precision of outcomes could be improved by considering age and the refractive index. SETTING: Vissum Instituto Oftalmologico de Alicante, Alicante, Spain. DESIGN: Case series. METHODS: Flaps were created using a mechanical microkeratome. The stromal refractive index was measured using a VCH-1 refractometer after flap lifting. Refractive data were obtained 1, 3, and 6 months postoperatively. RESULTS: Uneventful LASIK was performed in 133 eyes. The mean age, refractive index, and applied corrections were 33.4 years ± 9.49 (SD), 1.368 ± 0.006, and -2.43 ± 3.36 diopters (D), respectively. The difference between the predicted and actual postoperative refractive error = 2.315-0.021 age-1.106 refractive index (F = 3.647, r = 0.254, P=.029; n = 109) at 1 month and = 11.820-0.023 age-7.976 refractive index (F = 3.392, r = 0.261, P=.022, n = 106) at 3 months. A correlation between the actual and calculated postoperative refraction improved from r = -0.178 (P=.064; n = 75) to r = -0.418 (P<.001) after considering the true refractive index 6 months postoperatively. CONCLUSIONS: The predicted outcomes of LASIK can be improved by inputting the refractive index of the individual corneal stroma. Unexpected outcomes (>0.50 D) of LASIK could be avoided by considering patient age and the refractive index and by adjusting the applied correction accordingly.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".