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Record W2587604494 · doi:10.1093/ecco-jcc/jjx002.109

DOP072 Assessment of the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) using central video review of colonoscopies in paediatric patients with ulcerative colitis: data from the Canadian Children IBD Network

2017· article· en· W2587604494 on OpenAlexaffabout
Nicholas Carman, Hien Q. Huynh, Marialena Mouzaki, Eileen Crowley, Catharine M. Walsh, Amanda Ricciuto, Thomas D. Walters, Peter Church

Bibliographic record

VenueJournal of Crohn s and Colitis · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsSickKids FoundationUniversity of TorontoUniversity of AlbertaHospital for Sick Children
Fundersnot available
KeywordsMedicineColonoscopyIntraclass correlationUlcerative colitisInternal medicineInflammatory bowel diseaseGastroenterologyDiseaseColorectal cancerPsychometrics

Abstract

fetched live from OpenAlex

Background: The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) is a validated endoscopic tool which measures the worst disease activity in the rectosigmoid. This is potentially problematic as paediatric disease is often pancolonic and inflammation can be patchy, especially during active treatment. To date, there are no data evaluating the UCEIS in paediatrics. Using colonoscopy videos performed in patients from the Canadian Children IBD Network, we explored the reliability of the UCEIS when applied to colonic segments proximal to the recto-sigmoid. Methods: Video recordings of colonoscopies obtained from paediatric patients with UC undergoing endoscopic assessment at Network sites were utilised for the analysis. 4 IBD experts reviewed each video blinded to clinical information. For each anatomic colonic segment data encompassing the 3 elements of the UCEIS (bleeding, ulceration, vascular pattern) were recorded. Total UCEIS scores were calculated for each segment. In addition, the most distal segment with the highest score was identified (UCEIS-max). A global assessment of endoscopic lesion severity for the entire colon (GELS) was also recorded using a visual analogue scale. Inter-rater reliability (IRR) was measured using Intraclass correlation coefficients (ICCs). Correlation between scoring tools was measured using Spearman's test of correlation (r). Results: There was a broad range of endoscopic severity (median UCEIS 6 (range 3–8). The IRR for each aspect of the UCEIS are displayed in Table 1. The tool performed well throughout the colon, with “bleeding” being the variable demonstrating the most disagreement. When comparing standard UCEIS and UCEIS-max, in 33% of patients the maximally affected segment was proximal to the rectosigmoid. In 10% of these subjects the difference in UCEIS score was greater than 1 point (p<0.001). Correlation with GELS was better for UCEIS-max (r=0.79, p<0.001), than for standard UCEIS (r=0.68, p<0.001). Table 1. Inter-rater reliability for UCEIS variables across anatomical segments Conclusions: UCEIS is a valuable tool in the assessment of endoscopic disease severity in paediatric UC. UCEIS, when applied in standard fashion to the recto-sigmoid shows excellent IRR amongst IBD physicians. The tool can be applied across the colon, with only a small decrease in consistency. In this group of patients diagnosed with UC, one third of patients will have the maximally affected area proximal to the rectosigmoid, highlighting the importance of complete colonoscopy in assessing disease activity in UC.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.212
Threshold uncertainty score0.427

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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