Visual Outcomes of Cataract Surgery in Patients With Keratoconus Using Toric and Non-toric Lenses
Bibliographic record
Abstract
Purpose: To compare the accuracy and outcomes of different intraocular lens (IOL) power calculation formulas in eyes with keratoconus undergoing cataract surgery with toric and non-toric IOLs. Methods: This was a consecutive retrospective case series study including patients from the Cornea Service at the Department of Ophthalmology and Visual Sciences at the University of British Columbia, Vancouver, Canada, from 2000 to 2020. Keratoconus was diagnosed based on corneal topography and clinician opinion. Patients who underwent topography-guided photorefractive keratectomy, intracorneal ring segments implantation, or corneal transplant were excluded. The manifest spherical equivalent, prediction errors, and median absolute errors were calculated. Descriptive statistics were expressed as mean ± standard deviation. Results: There were 160 eyes from 101 patients; 136 eyes received non-toric lenses and 24 eyes received toric lenses. Most patients had mild disease (< 48.00 diopters [D]) when stratified by steep keratometry values. Patients with severe disease (> 53.00 D) were significantly more hyperopic following surgery ( P < .05). The Barrett Universal II (0.26 D, inter-quartile range [IQR] = 0.4), Holladay 2 (0.31, IQR = 1.2), and SRK/T (0.42, IQR = 0.86) formulas had the lowest median absolute error. The postoperative prediction error following toric lens insertion was not significantly different than following non-toric lens insertion, and the mean absolute astigmatism was significantly reduced with toric lenses. Conclusions: The Barrett Universal II, Holladay 2, and SRK/T were the most accurate IOL power calculation formulas in patients with keratoconus undergoing cataract surgery. Hyperopic surprise was increased in severe keratoconus. Toric IOLs may be considered in patients with mild keratoconus. [ J Refract Surg . 2023;39(5):319–325.]
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".