Predictive Validly of the Toronto IBD Global Endoscopic Reporting (TIGER) Score for Clinical Outcomes and Quality of Life Among Ulcerative Colitis and Crohn’s Disease Patients
Bibliographic record
Abstract
Aims The recently developed TIGER endoscopy score was established to reliably describe disease severity as it can be utilized for both Ulcerative Colitis (UC) and Crohn’s disease (CD) patients. 1 The aim of this study was to assess the TIGER score’s ability to predict outcomes regarding complications and quality of life of both UC and CD patients. Methods A cohort of 78 patients (UC n=40, and CD n=38) followed for 52-week in multiple visit prospective study. Each visit included patient interviews, disease specific quality of life IBD disk questionnaire, blood draws for C-reactive protein (CRP) mg/DL, and fecal calprotectin (FC) μg/g. Baseline total TIGER endoscopy scores were dichotomized as <100 (remission-mild activity) or ≥100 (moderate-severe activity) [ 1 ]. Results At baseline UC patients with TIGER scores ≥100 had significantly higher CRP, FC, and IBD disk. In CD patients, at baseline, compared to patients with TIGER scores <100, patients with TIGER scores ≥100 had significantly higher CRP, FC, and IBD disk. In terms of hospitalizations, at 52-weeks, patients with baseline TIGER scores ≥100 had a significantly increased likelihood of being hospitalized(p<0.02). In terms of side effects, at 52-weeks, compared to patients with baseline TIGER scores <100, UC and CD patients with baseline TIGER scores ≥100 had a significantly increased likelihood of reporting a side effect from both medications and disease complications (p<0.006). Conclusions The TIGER endoscopic score demonstrates significant association with CRP, FC and disease-specific quality of life IBD disk questionnaire in both UC and CD patients. Publication History Article published online: 14 April 2023 © 2023. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".