Evidence map›Paper›PMID 36692457›Full record

ArticleTranslational vision science & technology2023

Geometric Morphometrics Can Predict Postoperative Visual Acuity Changes in Patients With Epiretinal Membrane: A Retrospective Study.

Sugao Miyagi, Akio Oishi, Eiko Tsuiki, Takashi Kitaoka

Abstract read
In one paragraph

Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sugao MiyagiDepartment of Ophthalmology and Visual Sciences, Graduate School of Biomedical Sciences, Nagasaki University, 1-7-1 Sakamoto, Nagasaki, Japan.
Akio OishiDepartment of Ophthalmology and Visual Sciences, Graduate School of Biomedical Sciences, Nagasaki University, 1-7-1 Sakamoto, Nagasaki, Japan.
Eiko TsuikiDepartment of Ophthalmology and Visual Sciences, Graduate School of Biomedical Sciences, Nagasaki University, 1-7-1 Sakamoto, Nagasaki, Japan.
Takashi KitaokaDepartment of Ophthalmology and Visual Sciences, Graduate School of Biomedical Sciences, Nagasaki University, 1-7-1 Sakamoto, Nagasaki, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To investigate the efficacy of the geometric morphometrics method for the evaluation of retinal deformation in patients with epiretinal membrane (ERM) and determine whether the degree of deformation can serve as a predictive factor for postoperative visual outcome. Methods: We retrospectively evaluated data from 29 eyes of 29 patients with primary ERM. Preoperative optical coherence tomography images were compared with images of their normal fellow eyes using the geometric morphometrics thin-plate spline technique. Conventional parameters such as retinal layer thickness and previously reported indices were also measured. The correlation between the preoperative parameters and visual acuity was evaluated. Statistical comparisons were performed using a paired t-test, and associations between the optical coherence tomography image parameters and visual acuity were determined using Spearman's rank correlation coefficient. Results: Bending energy, which was calculated using geometric morphometrics, was significantly associated with visual acuity as well as conventional optical coherence tomography parameters and previously reported indices. Multiple regression analysis showed that bending energy was an independent predictive factor for postoperative visual acuity changes. Conclusions: The geometric morphometrics method is an effective approach for evaluating the severity of ERM and predicting the efficacy of surgery. Translational Relevance: Geometric morphometrics can effectively evaluate retinal deformation in eyes with epiretinal membrane.

Indexed as

Epiretinal MembraneHumansRetinaRetrospective StudiesTomography, Optical CoherenceVisual AcuityVitrectomy

Identifiers

PMID36692457
PMCPMC9896846

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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.