Clinical value of the Toronto inflammatory bowel disease global endoscopic reporting score in ulcerative colitis
Bibliographic record
Abstract
BACKGROUND Endoscopic evaluation in diagnosing and managing ulcerative colitis (UC) is becoming increasingly important. Several endoscopic scoring systems have been established, including the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score and Mayo Endoscopic Subscore (MES). Furthermore, the Toronto Inflammatory Bowel Disease Global Endoscopic Reporting (TIGER) score for UC has recently been proposed; however, its clinical value remains unclear. AIM To investigate the clinical value of the TIGER score in UC by comparing it with the UCEIS score and MES. METHODS This retrospective study included 166 patients with UC who underwent total colonoscopy between January 2017 and March 2023 at the Affiliated Hospital of Qingdao University (Qingdao, China). We retrospectively analysed endoscopic scores, laboratory and clinical data, treatment, and readmissions within 1 year. Spearman’s rank correlation coefficient, receiver operating characteristic curve, and univariate and multivariable logistic regression analyses were performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, United States) and GraphPad Prism version 9.0.0 for Windows (GraphPad Software, Boston, Massachusetts, United States). RESULTS The TIGER score significantly correlated with the UCEIS score and MES (r = 0.721, 0.626, both P < 0.001), showed good differentiating values for clinical severity among mild, moderate, and severe UC [8 (4–112.75) vs 210 (109–219) vs 328 (219–426), all P < 0.001], and exhibited predictive value in diagnosing patients with severe UC [area under the curve (AUC) = 0.897, P < 0.001]. Additionally, the TIGER (r = 0.639, 0,551, 0.488, 0.376, all P < 0.001) and UCEIS scores (r = 0.622, 0,540, 0.494, and 0.375, all P < 0.001) showed stronger correlations with laboratory and clinical parameters, including C-reactive protein, erythrocyte sedimentation rate, length of hospitalisation, and hospitalisation costs, than MES (r = 0.509, 0,351, 0.339, and 0.270, all P < 0.001). The TIGER score showed the best predictability for patients' recent advanced treatment, including systemic corticosteroids, biologics, or immunomodulators (AUC = 0.848, P < 0.001) and 1-year readmission (AUC = 0.700, P < 0.001) compared with the UCEIS score (AUC = 0.762, P < 0.001; 0.627, P < 0.05) and MES (AUC = 0.684, P < 0.001; 0.578, P = 0.132). Furthermore, a TIGER score of ≥ 317 was identified as an independent risk factor for advanced UC treatment (P = 0.011). CONCLUSION The TIGER score may be superior to the UCIES score and MES in improving the accuracy of clinical disease severity assessment, guiding therapeutic decision-making, and predicting short-term prognosis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".