A Randomized Control Study on Post-Operative Iris Distortion Following Small-Pupil Cataract Surgery Using B-HEX Pupil Expander Versus Malyugin Ring
Medical Journal Armed Forces India（2024）
Associate Professor (Ophthalmology)
Abstract
Background The aim of the study was to evaluate postoperative pupil distortion following small pupil cataract surgeries performed using B-HEX and Malyugin rings (MR). Methods A randomized control trial was conducted from June 2020 to June 2023 at a tertiary eye-care hospital. The study consisted of 64 participants for cataract surgery with small pupil. There were two groups, one undergoing surgery with the use of B-HEX pupil expander and other with MR intraoperatively and the rest of the surgery was proceeded as per the convention. Areas of preoperative and postoperative images was calculated, put into an online software and pupil distortion was calculated in percentage. Two-tailed t-test was used to see the difference between the two groups. Results Mean age at presentation was 70.5 ± 10.12 years. Most common cause for small pupil was tamsulosin therapy. Mean size of small-pupil was 3.0 ± 1.1 mm. With the application of two rings, mean pupillary area preoperatively was 4178.23 ± 1589.46 and postoperatively was 6100.44 ± 2658.28 following the use of MR, respectively and 30,002.93 ± 13,193.40 preoperatively and 37,648.26 ± 15,207.01 postoperatively following the use of B-Hex ring respectively. Comparing baseline area from pupillary area at 1-month follow-up, a significant increase was noted for both the rings. Also, MR caused significantly more pupillary distortion compared to B-HEX ring (p < 0.05). Conclusion MR causes significantly more pupillary distortion in the postoperative period compared to B-HEX ring. Though, both the rings cause pupillary distortion, these devices expand the surgical area adequately, ease the procedure, decrease risk of complications achieving good functional visual outcomes.
MoreTranslated text
Key words
Cataract surgery,Small pupil,Pupil expanders,Malyugin ring,B-HEX
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined