Evidence map›Paper›PMID 32143464›Full record

ArticleJournal of clinical medicine2020

Parafoveal and Peripapillary Perfusion Predict Visual Field Recovery in Chiasmal Compression due to Pituitary Tumors.

Ga-In Lee, Kyung-Ah Park, Sei Yeul Oh, Doo-Sik Kong

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Ga-In LeeDepartment of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
Kyung-Ah ParkDepartment of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
Sei Yeul OhDepartment of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
Doo-Sik KongDepartment of Neurosurgery, Endoscopic Skull Base Surgery Clinic, Brain Tumor Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.

Funding

National Research Foundation of Korea 2018R1D1A1B07043958
6 · The paper itself

Abstract

backgroundTo evaluate the potential of vessel density alterations for predicting postoperative visual field (VF) improvement in chiasmal compression using optical coherence tomography angiography (OCT-A).

methodsThe study investigated 57 eyes of 57 patients diagnosed with pituitary tumors and 42 eyes of 42 age and refractive error matched controls. All eyes with chiasmal compression for which preoperative optical coherence tomography (OCT) and OCT-A, and pre- and postoperative VF data were available. Preoperative vessel densities of superficial retinal capillary plexus (SRCP), deep retinal capillary plexus (DRCP), and radial peripapillary capillary (RPC) segment were utilized by OCT-A.

resultsPreoperative peripapillary retinal nerve fiber layer and ganglion cell layer complex thickness and vessel densities of SRCP and RPC segments in eyes with chiasmal compression were significantly reduced compared with healthy controls (

conclusionsStructural degeneration referred to as microvascular alterations measured by OCT-A and preoperative VF defects were associated with worse postoperative VF prognosis. Parafoveal and peripapillary vessel densities may serve as a sensitive, structural prognostic factors in the preoperative judgement of chiasmal compression.

Indexed as

chiasmal compressionoptical coherence tomography angiographyprognosisvisual field recovery

Identifiers

PMID32143464
PMCPMC7141271

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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.