Evidence map›Paper›PMID 38880871›Full record

ArticleBMC ophthalmology2024

Peripapillary RNFL cross-sectional area and its association with other parameters in a Chinese population.

Yan Yanni, Wang Qian, Wei Wenbin

Abstract read
In one paragraph

Article in BMC ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yan YanniBeijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
Wang QianBeijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
Wei WenbinBeijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China. weiwenbintr@163.com.

Funding

National Natural Science Foundation of China 82220108017R&D Program of Beijing Municipal Education Commission SM202310025005
6 · The paper itself

Abstract

backgroundQuantitative analysis of retinal nerve fibers is important for the diagnosis and treatment of optic nerve diseases. Peripapillary retinal nerve fiber layer (RNFL) cross-sectional area may give a more accurate quantitative assessment of retinal nerve fibers than RNFL thickness but there have been no previous reports of the peripapillary RNFL cross-sectional area or other parameters. The purpose of the current study was to determine peripapillary RNFL cross-sectional area and its association with other factors in an adult Chinese population.

methodsRNFL cross-sectional area was measured during peripapillary circular optical coherence tomography (OCT) scan with a diameter of 12° centered on the optic disc. Correlation between RNFL cross-sectional area and other parameters was evaluated by linear regression analysis in a cross-sectional study of an adult Chinese population.

resultsA total of 2404 eyes from 2404 subjects were examined. Multivariate linear regression analysis showed that larger RNFL cross-sectional area correlated with younger age (p < 0.001), female gender (p = 0.001), no history of diabetes (p = 0.012) and larger optic disc area (p < 0.001).

conclusionsPeripapillary RNFL cross-sectional area is correlated positively with optic disc area, suggesting that eyes with larger optic discs have thicker RNFL. Further studies are needed to confirm whether this correlation is due to differences in the numbers of retinal nerve fibers or other factors.

Indexed as

Nerve FibersOptic DiskRetinal Ganglion CellsTomography, Optical CoherenceAdultAgedChinaCross-Sectional StudiesEast Asian PeopleFemaleHumansMaleMiddle AgedYoung AdultOcular magnificationOptic disc areaPeripapillary RNFL cross-sectional area

Identifiers

PMID38880871
PMCPMC11181622

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.