Influence of Posterior Corneal Asphericity On Power Calculation Error After Laser In Situ Keratomileusis or Photorefractive Keratectomy for Myopia
Bibliographic record
Abstract
OBJECTIVES: To assess the impact of posterior corneal asphericity on postoperative calculation error using the Haigis-L and the Barrett formulas for eyes after laser in situ keratomileusis or photorefractive keratectomy (PRK). METHODS: We assessed the mean absolute error (MAE) of two power calculation formulas, Barrett true-K and Haigis-L formulas, in a retrospective analysis of 34 eyes of 34 patients who underwent cataract surgery. We performed a regression analysis between corneal parameters (anterior and posterior Q values, Kmax, K1, and K2) and the MAE of each formula. RESULTS: In the cohort, 11 eyes were of women and 23 of men. The average age of the study population was 66.5±8.6 years. The mean axial length was 24±4.7 mm, the mean anterior chamber depth was 3.27±0.7 mm, and the mean posterior Q-value was -0.15±0.28. The MAE of Haigis-L and Barrett true-K formulas were 0.72 and 0.68, respectively (P=0.54). The regression analysis showed a statistically significant relationship only between the error in refraction prediction and the posterior Q-value regardless of the formula used. The coefficient of determination was higher for the Barrett true-K formula (r=0.52; R2=0.28; P<0.05), compared with the Haigis-L (r=0.49; R2=0.25; P<0.05). CONCLUSIONS: Posterior corneal surface asphericity influences the refractive error of calculation using both Haigis-L and Barrett true-K formulas for eyes after a myopic PRK or laser-assisted in situ keratomileusis surgery.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".