Accuracy of keratoconus-specific formulae compared to standard formulae for intraocular lens power calculation in patients with keratoconus
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy of keratoconus-specific formulae for nontoric and toric intraocular lenses in eyes with keratoconus undergoing cataract surgery. DESIGN: Consecutive retrospective case series. PARTICIPANTS: Patients with keratoconus who underwent cataract surgery. METHODS: A retrospective chart review was conducted on cataract surgeries performed by the Cornea Service in the Department of Ophthalmology and Visual Sciences of the University of British Columbia from 2000 to 2023. The Kane keratoconus, Kane, Barrett Universal 2, Barrett True K, SRK II, SRK/T, Hoffer Q, Holladay I, EVO, Hill RBF and Hoffer QST, and Pearl DGS formulae were calculated. The postoperative mean absolute error (MAE) and mean prediction error (MPE) were calculated for each formula. RESULTS: A total of 133 eyes from 88 patients were eligible for inclusion in the study, 113 from 74 patients received nontoric IOLs, and 20 from 14 patients received toric IOLs. Pearl DGS had the most myopic MPE of -0.51 ± 1.04, which was statistically significant (p < 0.001) compared to all other formulae. There were no statistically significant differences in the MPE and MAE of the Kane keratoconus, Kane, Barrett Universal 2, Barrett True K keratoconus-specific formula, SRK II, SRK/T, Hoffer Q, Holladay I, EVO, Hill RBF and Hoffer QST formulae (p > 0.05). CONCLUSION: There was no difference in IOL power estimation accuracy with keratoconus-specific formulae compared to conventional formulae for cataract surgery in KC patients. The IOL power estimation in KC remains significantly less accurate compared with non-KC patients.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".