Evidence map›Paper›PMID 36766572›Full record

ReviewDiagnostics (Basel, Switzerland)2023

Linked Color Imaging for Stomach.

Eiji Umegaki, Hiraku Misawa, Osamu Handa, Hiroshi Matsumoto, Akiko Shiotani

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Chonnam medical journal · 2023
    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

5 authors.

Eiji UmegakiDepartment of Gastroenterology & Hepatology, Kawasaki Medical School, 577 Matsushima, Kurashiki-shi, Okayama 701-0192, Japan.ORCID 0000-0003-3724-2604
Hiraku MisawaDepartment of Gastroenterology & Hepatology, Kawasaki Medical School, 577 Matsushima, Kurashiki-shi, Okayama 701-0192, Japan.
Osamu HandaDepartment of Gastroenterology & Hepatology, Kawasaki Medical School, 577 Matsushima, Kurashiki-shi, Okayama 701-0192, Japan.
Hiroshi MatsumotoDepartment of Gastroenterology & Hepatology, Kawasaki Medical School, 577 Matsushima, Kurashiki-shi, Okayama 701-0192, Japan.
Akiko ShiotaniDepartment of Gastroenterology & Hepatology, Kawasaki Medical School, 577 Matsushima, Kurashiki-shi, Okayama 701-0192, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Image-enhanced endoscopy (IEE) plays an important role in the detection and further examination of gastritis and early gastric cancer (EGC). Linked color imaging (LCI) is also useful for detecting and evaluating gastritis, gastric intestinal metaplasia as a pre-cancerous lesion, and EGC. LCI provides a clear excellent endoscopic view of the atrophic border and the demarcation line under various conditions of gastritis. We could recognize gastritis as the lesions of the diffuse redness to purple color area with LCI. On the other hand, EGCs are recognized as the lesions of the orange-red, orange, or orange-white color area in the lesion of the purple color area, which is the surround atrophic mucosa with LCI. With further prospective randomized studies, we will be able to evaluate the diagnosis ability for EGC by IEE, and it will be necessary to evaluate the role of WLI/IEE and the additional effects of the diagnostic ability by adding IEE to WLI in future.

Indexed as

diffuse rednessearly gastric cancerendoscopic imaginggastric intestinal metaplasiagastritisimage-enhanced endoscopylavender color signlinked color imagingthe International Commission on Illumination (CIE) 1976 (L∗a∗b∗) color space

Identifiers

PMID36766572
PMCPMC9914129

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Registered trials

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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.