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.
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".