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Randomized controlled study of the prediction of diminutive/small colorectal polyp histology using didactic versus computer‐based self‐learning module in gastroenterology trainees

Abstract:

Background and Aim The aim of this randomized trial was to evaluate the performance of self‐training versus didactic training in order to increase the diagnostic accuracy of diminutive/small colonic polyp histological prediction by trainees.

Methods Sixteen trainees reviewed 78 videos (48 iSCAN‐OE and 30 NBI) of diminutive/small polyps in a pretraining assessment. Trainees were randomized to receive computer‐based self‐learning (n = 8) or didactic training (n = 8) using identical teaching materials and videos. The same 78 videos, in a different randomized order, were assessed. The NICE (NBI International Colorectal Endoscopic) and SIMPLE (Simplified Identification Method for Polyp Labeling during Endoscopy) classification systems were used to classify diminutive/small polyps.

Results A higher proportion of high‐confidence predictions of polyps was made by the self‐training group versus the didactic group using both the SIMPLE classification (77.1% [95% CI 73.4–80.3] vs 69.9% [95% CI 66.1–73.5%] [P = 0.005]) and the NICE classification (77% [95% CI 73.2–80.4%] vs 69.8% [95% CI 66–73.4%] [P = 0.006]). When using NICE, sensitivity of the self‐training group compared with the didactic group was 72% versus 83% (P = 0.0005), and the accuracy was 66.1% versus 69.1%. The training improved the confidence of participants and SIMPLE was preferred over NICE.

Conclusion Self‐learning for the prediction of diminutive/small polyp histology is a method of training that can achieve results similar to didactic training. Availability of adequate self‐learning teaching modules could enable widespread implementation of optical diagnosis in clinical practice.

Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1111/den.13389

Authors



Publisher:
Wiley
Host title:
Digestive Endoscopy
Journal:
Digestive Endoscopy More from this journal
Volume:
31
Issue:
5
Pages:
535-543
Publication date:
2019-04-08
Acceptance date:
2019-03-03
DOI:
EISSN:
1443-1661
ISSN:
0915-5635


Keywords:
Pubs id:
pubs:886871
UUID:
uuid:f017dd65-8328-46c2-981b-9512f7666408
Local pid:
pubs:886871
Source identifiers:
886871
Deposit date:
2019-08-09

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