Accuracy and predictability of intraocular lens power calculation after laser in situ keratomileusis
Bibliographic record
Abstract
PURPOSE: To study the accuracy and predictability of intraocular lens (IOL) power calculation in eyes that had laser in situ keratomileusis (LASIK). SETTING: Gimbel Eye Centre, Calgary, Alberta, Canada. METHODS: Refractive outcomes in 6 cataract surgery and lensectomy eyes after previous LASIK were analyzed retrospectively. Target refractions based on measured and refraction-derived keratometric values were compared with postoperative achieved refractions. Differences between target refractions calculated using 5 IOL formulas and 2 A-constants and achieved refractions were also compared. RESULTS: The refractive error of IOL power calculation in postoperative LASIK eyes was significantly reduced when refraction-derived keratometric values were used for IOL power calculation. Persistent residual hyperopia still occurred in some cases; this was corrected by hyperopic LASIK. Refractive results appeared more accurate and predictable when the Holladay 2 or Binkhorst 2 formula was used for IOL power calculation. CONCLUSION: Hyperopic error after cataract surgery in post-LASIK eyes was significantly reduced by using refraction-derived keratometric values for IOL power calculation. Persistent hyperopic error was corrected by hyperopic LASIK.
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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 | 0.001 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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 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".