Purpose: We evaluated the effect of optimization of the intraocular lens (IOL) power calculation formula SRK/T and Barrett Universal II (BU II) in long eyes (≥26 mm: group L) and short eyes (≤22 mm: group S) using axial length calculated from segmented refractive indices (SRI).
Setting: Multicenter study at five sites in Japan.
Design: Retrospective observational study.
Methods: This study included 461 eyes of 461 patients (mean age 73.8 ± 8.4 years) who underwent cataract surgery. The predicted refractive error (PRE) was compared between the SRI (ARGOS) and the equivalent refractive index (ERI) biometers (IOLMaster700). The patients were randomly divided into two groups, a learning group and a validation group. The optimization constants were determined in the learning group, and the optimization constants were subsequently applied to the validation group and compared with the ERI biometer results.
Results: Using both SRK/T and BU II, the validation group's PRE using optimization constants for the SRI biometer in group L was significantly smaller than that using the ERI biometer (p<0.001, p<0.01). In group L, the arithmetic PRE of Barrett UII formula with SRI showed a significant improvement after optimization compared to before optimization (p<0.0001). In group S, the arithmetic PRE of SRK/T and Barrett UII formula with SRI showed a significant improvement (p<0.0001, p<0.0001).
Conclusion: In long and short eyes, the current study revealed that optimization of the SRK/T and Barrett formula constants for the SRI biometer was beneficial to achieve accurate refractive outcomes after cataract surgery.
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http://dx.doi.org/10.2147/OPTH.S477006 | DOI Listing |
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Over the past decade, the size of neural network models has gradually increased in both breadth and depth, leading to a growing interest in the application of neural network pruning. Unstructured pruning provides fine-grained sparsity and achieves better inference acceleration under specific hardware support. Unstructured Pruning at Initialization (PaI) optimizes the iterative pruning pipeline, but sparse weights increase the risk of underfitting during training.
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College of Health Sciences, University of Memphis, Memphis, TN, USA.
Runners often reduce their pace during inclined running to maintain a constant metabolic workload, known as iso-efficiency speed (a speed-incline combination with the same metabolic intensity as level running). This study investigates changes in lower extremity (LE) joint work profiles when running on an incline at iso-efficiency speed. Eleven collegiate distance runners completed a treadmill running task under three conditions (0%, 4%, and 8% incline).
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