Prediction accuracy of IOL calculation formulas using the ASCRS online calculator for a diffractive extended depth-of-focus IOL after myopic laser in situ keratomileusis
Bibliographic record
Abstract
BACKGROUND: To evaluate IOL calculation formulas provided by an online calculation tool from the ASCRS for an extended depth-of-focus (EDOF) IOL after previous myopic laser in situ keratomileusis (LASIK). SETTING: Department of Ophthalmology, Goethe University, Frankfurt, Germany. DESIGN: Retrospective consecutive case series. METHODS: Patients who underwent cataract surgery or refractive lens exchange with implantation of a diffractive EDOF IOL and who had a history of myopic LASIK were included. Biometry, refractive data regarding the LASIK procedure, target refraction, and postoperative refraction were collected. Mean prediction error, mean absolute error (MAE), and the number of eyes within ±0.5 diopters (D), ±1.0 D, ±1.5 D, and ±2.0 D were calculated with the following formulas from the ASCRS calculator: Shammas, Haigis-L, Barrett True-K, Barrett No History, Masket, modified Masket, and the average of all formulas (average). RESULTS: Twenty-five eyes matched the inclusion criteria. Mean spherical equivalent (SE) was -0.81 ± 0.69 D; the mean pre-LASIK SE was -6.4 ± 3.63 D. The formulas ranked by MAE were Shammas (0.7 ± 0.75 D), Haigis-L (0.72 ± 0.57 D), average (0.79 ± 0.8 D), Barrett True-K (1.14 ± 0.89 D), modified Masket (1.4 ± 1.15 D), Barrett No History (1.45 ± 0.7D ), and Masket (1.64 ± 1.27 D). The formulas with the most eyes within ±0.5 D were average (52%), Shammas (48%), and Haigis-L (44%) formulas. CONCLUSIONS: Calculation of IOLs in eyes with a history of refractive surgery remains a challenge. In this study, the Shammas and Haigis-L formulas performed best regarding MAE and percentage of eyes within ±0.5 D; however, the average of all formulas delivered reasonable results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 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".