Optimization of biometry for best refractive outcome in cataract surgery
Bibliographic record
Abstract
High-precision biometry and accurate intraocular lens (IOL) power calculation have become essential components of cataract surgery. In clinical practice, IOL power calculation involves measuring parameters such as corneal power and axial length and then applying a power calculation formula. The importance of posterior corneal curvature in determining the true power of the cornea is increasingly being recognized, and newer investigative modalities that can estimate both the anterior and posterior corneal power are becoming the standard of care. Optical biometry, especially using swept-source biometers, with an accuracy of 0.01-0.02 mm, has become the state-of-the-art method in biometry. With the evolution of IOL formulas, the ultimate goal of achieving a given target refraction has also moved closer to accuracy. However, despite these technological efforts to standardize and calibrate methods of IOL power calculation, achieving a mean absolute error of zero for every patient undergoing cataract surgery may not be possible. This is due to inherent consistent bias and systematic errors in the measurement devices, IOL formulas, and the individual bias of the surgeon. Optimization and personalization of lens constants allow for the incorporation of these systematic errors as well as individual bias, thereby further improving IOL power prediction accuracy. Our review provides a comprehensive overview of parameters for accurate biometry, along with considerations to enhance IOL power prediction accuracy through optimization and personalization. We conducted a detailed search in PubMed and Google Scholar by using a combination of MeSH terms and specific keywords such as "ocular biometry," "IOL power calculations," "prediction accuracy of refractive outcome in cataract surgery," "effective lens position," "intraocular lens calculation formulas," and "optimization of A-constants" to find relevant literature. We identified and analyzed 121 relevant articles, and their findings were included.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".