Reliability of the Endoscopic Ultrasound Ulcerative Colitis (EUS-UC) score for assessment of inflammation in patients with ulcerative colitis
Bibliographic record
Abstract
Abstract Background and study aims Endoscopic ultrasound (EUS) may be a useful modality for disease assessment and risk stratification in ulcerative colitis. We assessed the reliability of a newly developed EUS index of inflammation called the EUS-Ulcerative Colitis (EUS-UC) score. Patients and methods The EUS-UC score components include total wall thickness, hyperemia, and depth of inflammation (DOI). Three blinded expert endosonographers assessed EUS videos of 58 patients with UC in triplicate. Intra- and inter-rater reliability of the hyperemia and DOI component scores were estimated using intra-class correlation coefficients (ICCs). Total wall thickness reliability estimates could not be assessed in this study. The ICCs were compared to the original indices from which they were derived. Results For hyperemia, the inter-class ICC was “moderate” at 0.556 (95 % CI = 0.434–0.651) and the intra class ICC was “almost perfect” at 0.884 (95 % CI = 0.835–0.920). The newly defined hyperemia score performed better than the original index from which is was derived. The DOI inter-class ICC was “fair” at 0.335 (95 % CI = 0.201–0.464), and the intra-class ICC was “substantial” at 0.732 (95 % CI = 0.642–0.802). The DOI reliability estimates were similar to the original index from which it was derived. Conclusions The hyperemia component of the EUS-UC score performed significantly better than the original index from which it was derived, but the reliability of the DOI component was suboptimal. Intra-class correlation was excellent for both components. The EUS-UC score is a promising instrument for assessment of UC and further validation is required.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".