Are we ready to use new endoscopic scores for ulcerative colitis?
Bibliographic record
Abstract
For ulcerative colitis (UC), the variability in inflammatory activity along the colon poses a challenge in management. The focus on achieving endoscopic healing in UC is evident, where the UC Endoscopic Index of Severity and Mayo Endoscopic Subscore are commonly used for evaluation. However, these indices primarily consider the most severely affected region. Liu et al recent study validates the Toronto Inflammatory Bowel Disease Global Endoscopic Reporting (TIGER) score offering a comprehensive assessment of inflammatory activity across diverse segments of the colon and rectum and a reliable index correlating strongly with UC Endoscopic Index of Severity and moderately with Mayo Endoscopic Subscore (MES). Despite recommendation, certain aspects warrant further investigation. Fecal calprotectin, an intermediate target, correlates with TIGER and should be explored. Determining TIGER scores defining endoscopic remission and response, evaluating agreement with histological activity, and assessing inter-endoscopist agreement for TIGER require scrutiny. Exploring the correlation between TIGER and intestinal ultrasound, akin to MES, adds value.
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 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 | 0.047 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".