Evidence map›Paper›PMID 35814973›Full record

ReviewVisceral medicine2022

Today's Mistakes and Tomorrow's Wisdom… In Barrett's Surveillance.

Pauline A Zellenrath, Carlijn A M Roumans, Manon C W Spaander

Open access · bronzeAbstract readReview
In one paragraph

Review in Visceral medicine, 2022. 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
0.2field-weighted citation impact, top 51% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

3 authors at 1 institution in 1 country.

Pauline A ZellenrathDepartment of Gastroenterology and Hepatology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Carlijn A M RoumansDepartment of Gastroenterology and Hepatology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Manon C W SpaanderDepartment of Gastroenterology and Hepatology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Erasmus MC · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Barrett's esophagus (BE) is the only known precursor lesion of esophageal adenocarcinoma, a malignancy with increasing incidence and poor survival rates. To reduce mortality, regular endoscopic surveillance of BE patients is recommended to detect neoplasia in an (endoscopically) curable stage. In this review, we aim to provide an overview of current BE surveillance strategies, its pitfalls, and potential future directions to optimize BE surveillance. Summary: Several societal guidelines provide surveillance strategies. However, when practicing those endoscopies multiple drawbacks are encountered. Important challenges are time-consuming biopsy protocols with low adherence rates, biopsy sampling error, interobserver variability in endoscopic detection of lesions, and interobserver variability in diagnosis of dysplasia. Furthermore, the overall efficacy and cost-effectiveness of surveillance are questioned. Using novel techniques, such as artificial intelligence and personalized surveillance intervals, can help to overcome these obstacles. Key Messages: Currently, there is room for improvement in BE surveillance. Better risk-stratification is expected to reduce both patient and healthcare burdens. Personalized and dynamic surveillance intervals accompanied by novel techniques in detection and histopathological assessment of dysplasia may be tools for a change in the right direction.

Indexed as

Barrett's esophagusCost-effectivenessDysplasiaRisk-stratificationSurveillance

Identifiers

PMID35814973
PMCPMC9209968
OpenAlexW4214930528

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.