Comparison of the Barrett Universal II formula to previous generation formulae for paediatric cataract surgery
Bibliographic record
Abstract
PURPOSE: To compare the accuracy of the Barrett Universal II (BUII) five-variable formula to previous generation formulae in calculating intraocular lens (IOL) power following paediatric cataract extraction. METHODS: Retrospective study of consecutive paediatric patients who underwent uneventful cataract extraction surgery along with in-the-bag IOL implantation between 2012 and 2018 in the Hospital for Sick Children, Toronto, Ontario, Canada. The accuracy of five different IOL formulae, including the BUII, Sanders-Retzlaff-Kraff Theoretical (SRK/T), Holladay I, Hoffer Q and Haigis, was evaluated. Constant optimization was performed for each IOL and for each formula separately. Mean prediction error (PE) and the mean and median absolute PE (APE) were calculated for the five different IOL formulae investigated. RESULTS: Sixty-six eyes of 66 children (59% males) with a median age at surgery of 6.2 years (IQR, 3.2-9.2 years) were included in the study. The mean IOL power that was implanted was 23.3 ± 5.1 D (range; 12.0-39.0 D). Overall, the BUII had a comparable median APE to the Hoffer Q, Holladay I, SRK/T and Haigis formulae (BUII: 0.49D versus 0.48D, 0.61D, 0.74D and 0.58D respectively; p = 0.205). The BUII, together with Hoffer Q, produced better predictability within 0.5D from target refraction compared with the SRK/T formula (BUII:51.5%, Hoffer Q:51.5% versus SRK/T:31.8%, p = 0.002 for both). CONCLUSION: The BUII formula had comparable accuracy to other tested formulae and outperformed the SRK/T formula, when calculating IOL power within the 0.5D range from target refraction in paediatric eyes undergoing cataract surgery with in-the-bag IOL implantation.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".