The Development of a Thick-Lens Post–Myopic Laser Vision Correction Intraocular Lens Calculation Formula
Bibliographic record
Abstract
PURPOSE: To describe the development of the post-myopic laser vision correction (LVC) version of the PEARL-DGS intraocular lens (IOL) calculation formula and to evaluate its outcomes on an independent test set. DESIGN: Retrospective, single-center case series. METHODS: A modified lens position prediction algorithm was designed along with methods to predict the posterior corneal curvature radius and correct the corneal power measurement error. A different set of previously operated eyes that underwent LVC was used to evaluate the prediction precision of the post-LVC formula. RESULTS: Post-LVC PEARL-DGS formula significantly reduced mean absolute error of prediction in comparison to Haigis-L, Shammas, and American Society of Cataract and Refractive Surgery (ASCRS) average formulas (P < .001). It exhibited similar postoperative refractive precision as the Barrett True-K No History formula (P = .61). CONCLUSION: The post-LVC formula development process described in this article performed as well as the state-of-the-art post-LVC formula on the present test set. Further studies are required to assess its efficacy in other independent sets.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".