Optimization of biometry for best refractive outcome in cataract surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it