The Toronto IBD Global Endoscopic Reporting [TIGER] Score: A Single, Easy to Use Endoscopic Score for Both Crohn’s Disease and Ulcerative Colitis Patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: We constructed the Toronto IBD Global Endoscopic Reporting [TIGER] score for inflammatory bowel disease [IBD]. The aim of our study was to develop and validate the TIGER score against faecal calprotectin [FC], C-reactive protein [CRP], and IBD Disk. METHODS: A cross-sectional study was performed among 113 adult patients (60 Crohn's disease [CD] and 53 ulcerative colitis [UC]). In the development and usability phase, blinded IBD experts reviewed and graded ileocolonoscopy videos. In the validity phase the TIGER score was compared with: [1] the Simple endoscopic Score for CD [SES-CD] and the Mayo endoscopic score in CD and UC, respectively; [2] FC and CRP; and [3] IBD Disk. RESULTS: Inter-observer reliability of the TIGER score per segment between reviewers was excellent: interclass correlation coefficient [ICC] = 0.94 [95% CI: 0.92-0.96]. For CD patients, overall agreement per segment between SES-CD and TIGER was 91% [95% CI: 84-95] with kappa coefficient 0.77 [95% CI: 0.63-0.91]. There was a significant correlation between TIGER and CRP [p <0.0083], and TIGER and FC [p <0.0001]. In addition, there was significant correlation between TIGER and IBD Disk [p <0.0001]. For UC patients, overall agreement per segment between Mayo endoscopic score and TIGER was 84% [95% CI: 74%-90%] and kappa coefficient 0.60 [95% CI: 0.42-0.808]. There was a significant correlation between TIGER and FC [p <0.0001]. There was a significant correlation between TIGER and IBD Disk [p <0.0001]. CONCLUSIONS: The TIGER score is a reliable and simple novel endoscopic score that can be used for both CD and UC patients and captures full endoscopic disease burden.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".