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 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 | 0.002 | 0.013 |
| 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".