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 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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".