Evidence map›Paper›PMID 42592621›Full record

ReviewTranslational gastroenterology and hepatology2026

Optimizing detection and resection of colorectal polyps.

Rahul Karna, Mohammad Bilal, Aasma Shaukat

Abstract readReview
In one paragraph

Review in Translational gastroenterology and hepatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Rahul KarnaCenter for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Mohammad BilalDivision of Gastroenterology & Hepatology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Aasma ShaukatDivision of Gastroenterology & Hepatology, New York University Grossman School of Medicine, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colonoscopy is the most widely performed endoscopic procedures in the United States and considered as primary screening and surveillance modality for colorectal cancer (CRC) prevention. Effective screening and surveillance requires prompt recognition of colorectal polyps, optical diagnosis of predicted histology and safe and effective polypectomy. We aim to perform a comprehensive review of the current evidence on tools and techniques to improve polyp detection and best practices to optimize endoscopic removal. Utilization of water assisted colonoscopy, artificial intelligence (AI), image enhanced endoscopy (IEE) and distal attachment devices can improve polyp detection. Underwater endoscopic mucosal resection (EMR) has emerged as a popular technique with potentially better outcomes than conventional EMR. Modification of EMR techniques including tip-in EMR and pre-cutting EMR have also been summarized. Endoscopic full thickness resection (EFTR) is an emerging technique other than endoscopic submucosal dissection (ESD) for suitable lesions harboring early submucosal cancer. Further, we also outline the common adverse events associated with polypectomy including bleeding, perforation, post-polypectomy syndrome, and discuss preventative measures to mitigate these adverse events. Eventually, the goal of colonoscopy is to reduce interval CRC incidence and mortality, and the technology that reduces post colonoscopy CRC incidence and techniques that improve safety and recurrence would see earlier adoption in the real world practice.

Indexed as

colonoscopyColorectal cancer (CRC)polypsquality

Identifiers

PMID42592621
PMCPMC13466877

What OpenQuestion holds

Textmetadata
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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.