Refractive Accuracy of Barrett True-K vs Intraoperative Aberrometry for IOL Power Calculation in Post-Corneal Refractive Surgery Eyes
Bibliographic record
Abstract
PURPOSE: To compare the refractive predictability of intraoperative aberrometry (IA, ORA, Alcon) and Barrett True-K/Universal II formulas for intraocular lens (IOL) power calculations in post-corneal refractive surgery and normal eyes. METHODS: Retrospective study of normal and post-corneal refractive surgery eyes that underwent cataract surgery with IA at tertiary academic center. Preoperatively, IOL power calculations were performed using Barrett Universal II (normal eyes) or Barrett True-K (post-corneal refractive surgery eyes) formulas. Intraoperatively, aphakic IA measurements were used for IOL power calculations. Mean absolute refractive prediction error (MAE) and the percentage of eyes with prediction error within ±0.50, ±0.75 and ±1.00 D were calculated. Refractive predictability was also evaluated in short, normal, and long eyes. RESULTS: Two hundred and seventy-three eyes were included in the analysis. No statistically significant differences were observed between the MAE of preoperative formulas and IA for post-hyperopic laser vision correction (LVC), post-myopic LVC, post-radial keratotomy (RK) and normal eyes. For prediction error within ±0.5 D in post-corneal refractive surgery eyes, range of agreement between Barrett True-K and IA ranged from 28% (7/25) of the time in post-RK eyes to 49% (40/81) of the time in post-hyperopic LVC; the corresponding value for Barrett Universal II/IA was 62% (64/103) in normal eyes. When there was disagreement, IA outperformed Barrett True-K in post-hyperopic LVC eyes and Barrett formula outperformed IA in post-myopic LVC, post-RK, and normal eyes. CONCLUSION: IA appears to be comparable to Barrett formulas for IOL power calculations in post-corneal refractive surgery and normal eyes. In post-hyperopic LVC, IA yields better results compared to Barrett True-K formula; in real-life scenarios, IA reveals statistical advantage over the Barrett True-K no history formula for eyes post-hyperopic LVC.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".