Evidence map›Paper›PMID 33204524›Full record

ArticleJournal of ophthalmology2020

Quantitative Analysis of Macular Retina Using Light Reflection Indices Derived from SD-OCT for Pituitary Adenoma.

Min Sun, Hongming Zhang, Xinjian Chen, Qimi Zhang

Open access · goldAbstract read
In one paragraph

Article in Journal of ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.1field-weighted citation impact, top 59% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
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 at 2 institutions in 1 country.

Min SunDepartment of Electronic and Information Engineering, Wenzheng College of Soochow University, Suzhou, Jiangsu, China.ORCID https://orcid.org/0000-0002-7822-6048
Hongming ZhangDepartment of Electronic Engineering, Jiangsu Vocational College of Electronics and Information, Huaian, Jiangsu, China.
Xinjian ChenSchool of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu, China.
Qimi ZhangDepartment of Electronic and Information Engineering, Wenzheng College of Soochow University, Suzhou, Jiangsu, China.
Soochow University · CNJiangsu Vocational College of Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo quantitatively investigate the macular retinal light reflection characteristic using optical property indices derived from spectral-domain optical coherence tomography (SD-OCT) scans with depth attenuation compensation for pituitary adenoma.

methodsThis study included 38 patients (mean age 44.66 ± 13.77 years old) with diagnosis of pituitary adenoma and 43 age-matched controls. All SD-OCT scans were light attenuation compensated by a depth-resolved model. Attenuation coefficient, the corrected intensity, and the retinal layer thickness were deduced for macular retinal nerve fiber layer (RNFL) and ganglion cell layer combined with inner plexiform layer (GCIPL), as well as comparing between patients and controls by statistical methods.

resultsAttenuation coefficients of RNFL and GCIPL among patients were significantly lower compared to the controls with

conclusionsThe retinal light reflection characteristics were debilitated in patients with pituitary adenoma. The potential of attenuation coefficients of RNFL and GCIPL in distinguishing patients with pituitary adenoma from controls was validated by the comparison of the derived optical property indices.

Identifiers

PMID33204524
PMCPMC7658689
OpenAlexW3096880144

What OpenQuestion holds

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Registered trials

None linked

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.