P578 Inter-observer agreement of an expert panel for gastrointestinal ultrasound in ulcerative colitis
Bibliographic record
Abstract
Abstract Background Gastrointestinal ultrasound (GIUS) is increasingly performed in inflammatory bowel disease to assess disease activity and treatment response. It is promising as an effective point-of-care imaging tool since it correlates well with endoscopy and other cross-sectional imaging modalities. Previous studies showed moderate to substantial interobserver agreement in Crohn’s disease. However, in ulcerative colitis (UC) inter-observer agreement for GIUS has not yet been evaluated. Therefore, we conducted a study to assess inter-observer agreement in UC. Methods Thirty patients with UC (five with clinically quiescent and 25 with active disease) were included in this study. Cine-loops were recorded for the sigmoid colon (SC) in a longitudinal and cross-sectional axis in B-mode and in colour Doppler mode. Cine-loops were scored by five independent raters blinded for clinical disease activity. The cine-loops were scored for bowel wall thickness (BWT), Doppler activity (0=no activity, 1=small spots limited to the bowel wall, 2=long stretches within the bowel wall, 3=long stretches within and outside of the bowel wall), inflammatory fat, bowel wall stratification, loss of haustration and lymph nodes (present or absent). The intraclass correlation coefficient was used for the assessment of bowel wall thickness. Fleiss’ kappa was used for all nominal variables and weighted Cohen’s kappa was used for all ordinal variables. Results Inter-observer agreement was good for bowel wall thickness (ICC: 0.7, 95% CI: 0.51–0.83, p < 0.0001) [1] and moderate for Doppler signal (k=0.57, 95% CI: 0.37–0.77, p < 0.0001) [2]. When Doppler signal was interpreted as absent (0) or present (1–3) the observed agreement was almost perfect (k=0.81, 95% CI: 0.69–0.92). For inflammatory fat the observed agreement was moderate (k=0.42, 95% CI: 0.29–0.58, p < 0.0001). Inter-observer agreement was fair for the presence of lymph nodes (k=0.35, 95% CI:0.20–0.49, p < 0.0001) and loss of stratification (k=0.22 95% CI: 0.09–0.35, p < 0.001). Agreement was slight for loss of haustrations (k=0.15, 95% CI: 0.00–0.29, p = 0.046). Conclusion GIUS is a reliable imaging modality with good to moderate interobserver agreement for BWT, vascularisation and fatty wrapping in UC. These ultrasonographic parameters are important features to distinguish active from quiescent disease. References
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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 | 0.061 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".