A Reliability Study: Strong Inter-Observer Agreement of an Expert Panel for Intestinal Ultrasound in Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Intestinal ultrasound [IUS] is a promising and non-invasive cross-sectional imaging modality in the diagnosis and monitoring of ulcerative colitis [UC]. Unlike endoscopy, where standardized scoring for evaluation of disease activity is widely used, scoring for UC with IUS is currently unavailable. Therefore, we conducted a study to assess the reliability of IUS in UC among expert sonographists in order to identify robust parameters. METHODS: Thirty patients with both clinically active [25] and quiescent [five] UC were included. Six expert sonographers first agreed upon key IUS parameters and grading, including bowel wall thickness [BWT], colour Doppler signal [CDS], inflammatory fat [i-fat], loss of bowel wall stratification [BWS], loss of haustrations and presence of lymph nodes. Thirty video-recorded cases were blindly reviewed. RESULTS: Inter-observer agreement was almost perfect for BWT (intra-class correlation coefficient [ICC]: 0.96) and substantial for CDS [κ = 0.63]. Agreement was moderate for presence of lymph nodes [κ = 0.41] and fair for presence of i-fat [κ = 0.36], BWS [κ = 0.24] and loss of haustrations [κ = 0.26]. Furthermore, there was substantial agreement for presence of disease activity on IUS [κ = 0.77] and almost perfect agreement for disease severity [ICC: 0.93]. Most individual parameters showed a strong association with IUS disease activity as measured by the six readers. CONCLUSION: IUS is a reliable imaging modality to assess disease activity and severity in UC. Important individual parameters such as BWT and CDS are reliable and could be incorporated in a future UC scoring index. Standardized acquisition and assessment of UC utilizing IUS with established reliability is important to expand the use of IUS globally.
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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 | 0.080 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".