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      • Record: found
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      Effect of time to operative repair within twenty-four hours on visual acuity outcomes for open globe injuries

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          Most cited references19

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          Visual acuities "hand motion" and "counting fingers" can be quantified with the freiburg visual acuity test.

          The visual acuity (VA) of patients with very low vision is classified using the semiquantitative scale "counting fingers" (CF), "hand motion" (HM), "light perception" (LP), and "no light perception." More quantitative measures would be desirable, especially for clinical studies. The results of clinical VA measurements, Early Treatment Diabetic Retinopathy Study (ETDRS) charts, and the Freiburg Visual Acuity Test (FrACT) were compared. The FrACT is a computerized visual acuity test that can present very large Landolt C optotypes when necessary. Examined were 100 eyes of 100 patients with various eye diseases (e.g., diabetic retinopathy, ARMD), covering a range of VAs from LP to decimal 0.32. The FrACT optotypes were presented on a 17-inch LCD monitor with random orientation. After extensive training, two ETDRS and FrACT measurements were obtained. The testing distance was 50 or 100 cm. ETDRS and FrACT coincided closely for VA > or = 0.02 (n = 80). ETDRS measures were successfully obtainable down to CF (at 30 cm; test-retest averaged over all patients, coefficient of variation [CV](ETDRS) = 9% +/- 8%), and FrACT provided reproducible measurements down to HM (test-retest CV(FrACT) =12% +/- 11%). For CF (n = 6), both ETDRS and FrACT resulted in a mean VA of 0.014 +/- 0.003 (range, 0.01-0.02). The VA results of FrACT for HM (n = 12) were 0.005 +/- 0.002 (range, 0.003-0.009); the individual values were highly reproducible. No results were obtainable for LP (n = 2). The three acuity procedures concur above a VA of 0.02. The results suggest that the category CF at 30 cm can be replaced by 0.014, using ETDRS or FrACT. Using FrACT, one can even reproducibly quantify VA in the HM-range, yielding a mean VA of 0.005.
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            The Ocular Trauma Score (OTS).

            Only based on a standardized terminology of ocular trauma terms, and using a very large number of injuries treated by a wide variety of ophthalmologists, could a reliable method be developed so that the functional outcome of a serious eye injury can be predicted with reasonable certainty. The authors used the databases of the United States and Hungarian Eye Injury Registries and, with a grant from the National Center for Injury Prevention at the Centers for Disease Control and Prevention, designed such a system.
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              The Birmingham Eye Trauma Terminology system (BETT).

              To evaluate the international eye injury scene and design a standardized terminology for mechanical eye injuries. Surveys of practicing ophthalmologists and an extensive review of the international ocular trauma literature. Development of the Birmingham Trauma Terminology (BETT) using a logic-based approach. BETT always uses the entire globe as the tissue of reference. Its well-defined terms encompass all types of mechanical eye injury. A one-to-one relationship exists between terms and clinical conditions. BETT provides an unambiguous, consistent, simple, and comprehensive system to describe any type of mechanical globe trauma. Endorsed by several societies and peer-reviewed journals as the standardized international language of ocular traumatology, BETT is expected to become the preferred terminology for categorizing eye injuries in daily clinical practice.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Eye
                Eye
                Springer Science and Business Media LLC
                0950-222X
                1476-5454
                August 2023
                December 21 2022
                August 2023
                : 37
                : 11
                : 2351-2355
                Article
                10.1038/s41433-022-02350-6
                aabb8f74-543d-4048-892b-b7191712d857
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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