Construct Validity of the Toronto IBD Global Endoscopic Reporting Score Compared to Inflammatory Biomarkers After 12-Month Follow-Up
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Toronto IBD Global Endoscopic Reporting (TIGER) score is a single endoscopic scoring index for both patients with Crohn's disease (CD) and ulcerative colitis (UC). The goal of this study was to assess the direct relationship between TIGER scores and fecal calprotectin (FC) and C-reactive protein (CRP) after 12 months. METHODS: A prospective 12-month study conducted in 1 tertiary IBD center. Baseline colonoscopy was performed. Moderate-to-severe mucosal involvement was defined as a TIGER score ≥ 100, Simple Endoscopic Score for Crohn's disease ≥ 6, Mayo Endoscopic Score > 1. FC and CRP levels were documented at each visit. Baseline TIGER, SES-CD, and MES were utilized as a predictor for FC and CRP levels after 12 months. RESULTS: The study population included 107 adults, 52 with CD and 55 with UC. A baseline TIGER score ≥ 100 had a sensitivity and specificity of 0.964 and 0.941, respectively, at predicting a patient having an FC level ≥ 800 μg/g and/or a CRP level ≥ 1.0 mg/dL at 12-month follow-up. A baseline TIGER score ≥ 100 was associated with increased likelihood of having FC > 100 μg/g (P < .001), and ≥ 800 μg/g (P < .001) after 12 months, despite receiving advanced therapy, after the 12-month follow-up period. Similar trends were observed with the SES-CD and MES. CONCLUSIONS: The TIGER score is a simple endoscopic score that can be used for both CD and UC that has been shown to have both construct validity with inflammatory marker and noninferiority to the current best-referenced endoscopic scores over a 12-month follow-up period. Future studies should begin incorporating TIGER as a measure of clinical response to interventions and therapeutics.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".