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Record W4213205290 · doi:10.1093/jcag/gwab049.167

A168 UTILITY OF FOLLOW-UP PANCOLONIC DYE SPRAY CHROMOENDOSCOPY FOR THE DETECTION AND TREATMENT OF DYSPLASTIC LESIONS IDENTIFIED DURING HIGH-DEFINITION WHITE-LIGHT ENDOSCOPY IN PATIENTS WITH COLONIC INFLAMMATORY BOWEL DISEASE

2022· article· en· W4213205290 on OpenAlexaffabout
Claudia Dziegielewski, Samir Gupta, Jeffrey D. McCurdy, Richmond Sy, Nav Saloojee, Sanjay K. Murthy

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsOttawa HospitalUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsChromoendoscopyDysplasiaMedicineInflammatory bowel diseaseGastroenterologyColonoscopyUlcerative colitisEndoscopyInternal medicineColorectal cancerDiseaseCancer

Abstract

fetched live from OpenAlex

Abstract Background Patients with inflammatory bowel disease (IBD) involving the colorectum are at higher risk for the development of flat and poorly delineated dysplastic lesions. Pancolonic dye spray chromoendoscopy (DCE) can represent an adjunct to white light endoscopy (WLE) that enhances detection and delineation of such lesions. Aims We evaluated the utility of follow-up DCE for lesion detection and treatment in patients with IBD who had visible (polypoid or flat lesions) or invisible (identified in non-targeted biopsies) dysplasia detected during high-definition (HD)-WLE. Methods We retrospectively studied patients with colonic IBD from The Ottawa Hospital who underwent DCE by a trained endoscopist, as follow-up for dysplasia detected during HD-WLE, over a 7-year time period (2013–2020). We collected demographic and disease-specific variables. Results Twenty-four patients were included (mean age 56.7±13.8 years, 50.0% male, 70.8% ulcerative colitis, mean disease duration 18.0±11.0 years, 70.8% moderate or severe disease activity historically, and 41.7% remote history of dysplasia). Seventeen (70.8%) patients were referred following detection of invisible dysplasia; the remainder (29.2%) were referred for surveillance of poorly defined visible dysplastic lesions. For those referred following detection of invisible dysplasia, DCE identified visible dysplasia at the same site in 8/17 (47.1%) patients, at a different site in 6/17 (35.3%) patients, and no dysplasia in 3/17 (17.6%) patients. DCE identified 1.39±1.50 new visible dysplastic lesions per patient that were not identified on index HD-WLE. DCE upgraded the highest grade of dysplasia and diagnosed colorectal cancer in 2/24 (8.3%) patients, which was missed on index HD-WLE. Endoscopic resection was successful in 23/34 (76.7%) dysplastic lesions identified on DCE. However, DCE was unable to facilitate lesion resection in three patients due to advanced lesion characteristics. Follow-up data was available for 17/24 (70.8%) patients (mean 0.88±0.58 years). Of these patients, 7/17 (41.2%) developed subsequent dysplasia, including 4/17 (23.5%) at a site of prior invisible dysplasia, 2/17 (11.8%) at sites of prior visible dysplasia, and 1/17 (5.88%) at a different site. One of these patients developed high-grade dysplasia requiring resection. Conclusions In this study, DCE is a valuable tool for dysplasia detection and treatment in persons with colonic IBD who have ill-defined dysplasia identified during HD-WLE. Given some patients developed dysplasia following DCE in our cohort, it is important to maintain close follow-up and obtain biopsies around sites of prior invisible and visible dysplasia. Further studies are required to validate our findings. Funding Agencies None

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.193
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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