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Record W2586981717 · doi:10.1093/ecco-jcc/jjx002.253

P127 An assessment of a pancolonic adaptation of the ulcerative colitis endoscopic index of severity in comparison to clinical and biochemical markers of disease activity in paediatric ulcerative colitis

2017· article· en· W2586981717 on OpenAlexaff
Amanda Ricciuto, J. Fish, Nicholas Carman, Eileen Crowley, Aleixo M. Muise, Peter Church, Thomas D. Walters, Binita M. Kamath, Anne M. Griffiths

Bibliographic record

VenueJournal of Crohn s and Colitis · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsUlcerative colitisMedicineInternal medicineGastroenterologyPancolitisColonoscopyFaecal calprotectinRectumInflammatory bowel diseasePopulationDiseaseCalprotectinColorectal cancerCancer

Abstract

fetched live from OpenAlex

Background: The ulcerative colitis Endoscopic Index of Severity (UCEIS) is an endoscopic index of disease activity, applied to the most severely affected region of the rectosigmoid, developed for use in adult ulcerative colitis (UC) patients. Given that most paediatric UC cases involve extensive colitis, we hypothesized that a pancolonic adaptation of the UCEIS may be more appropriate for children. We aimed to compare such an adapted version and the traditional UCEIS to clinical and biological markers of disease activity in this population. Methods: In this single-centre prospective study, Paediatric UC Activity Index (PUCAI), physician global assessment (PGA), fecal calprotectin (FC) and C-reactive protein (CRP) were measured in consecutive paediatric UC patients undergoing colonoscopy. Colonoscopies were scored by 2 blinded IBD physicians using the traditional rectosigmoid UCEIS and a pancolonic adaptation derived by summing individual UCEIS scores applied to the rectum, left colon, transverse colon and right colon, and dividing by the number of visualized segments. Spearman correlations were calculated between variables. The ability of both UCEIS versions to discriminate between disease activity, reflected by PUCAI, was assessed graphically. Results: 35 UC patients were enrolled (53% male, median age 12.7 years, 72% pancolitis). Median PUCAI score, FC and CRP were 15 (range 0–75), 1435 (range 29–16782) μg/g and 1.4 (range 0.1–17.1) mg/L, respectively. Spearman correlations are shown in Table 1. Both versions of the UCEIS demonstrated similar correlations with PGA, PUCAI, FC and CRP. Both scores were most highly correlated with PUCAI, and moderately correlated with PGA and FC. Correlations were lower with CRP. The boxplot in Figure 1 illustrates the distribution of the rectosigmoid and pancolonic UCEIS per disease activity category defined by established PUCAI cut-offs. Based on this, the rectosigmoid UCEIS appears to discriminate disease activity better than the colonic UCEIS. Table 1. Spearman correlations between traditional and pancolonic UCEIS and clinical and biochemical markers of UC activity Figure 1. Box plots illustrating the distribution of the rectosigmoid and pancolonic UCEIS per disease activity category based on PUCAI. Conclusions: The traditional UCEIS applied to the rectosigmoid region and an adapted pancolonic version performed similarly in this paediatric UC cohort when compared to various symptom-based and biological markers of disease activity. This likely relates to the usually homogeneous pattern of disease in paediatric UC in the rectum and more proximally in the colon.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.342
Teacher spread0.323 · 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 routes1
Has abstractyes

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