Effect of Standardised Scoring Conventions on Inter-rater Reliability in the Endoscopic Evaluation of Crohn’s Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Crohn's Disease Endoscopic Index of Severity [CDEIS] and Simplified Endoscopic Score for Crohn's Disease [SES-CD] demonstrate consistent overall intra- and inter-rater reliability. However, the reliability of some index items is relatively poor. We evaluated scoring conventions to improve the reliability of these items. METHODS: Five gastroenterologists with no previous experience scoring the CDEIS or SES-CD were trained on their use. A total of 65 video recordings of colonoscopies were scored blindly by each gastroenterologist before and after additional training on index scoring conventions. Intra-class correlation coefficients [ICCs] assessed the effect of application of these conventions on the reliability of the CDEIS, SES-CD, and a Global Evaluation of Lesion Severity [GELS] score. RESULTS: Following training on scoring conventions, inter-rater ICCs (95% confidence interval [CI]) for the total SES-CD score increased from 0.78 [0.71, 0.85] to 0.85 [0.79, 0.89]. The ICCs for the total CDEIS and GELS scores were not affected: corresponding inter-rater ICCs were 0.74 [0.65, 0.81] and 0.49, [0.38, 0.61] before and 0.73 [0.65, 0.81] and 0.53 [0.42, 0.64] following application of scoring conventions. Estimations of ulcer depth, surface area, anatomical location, and stenosis were important sources of variability. CONCLUSIONS: Use of scoring conventions previously developed by expert central readers enhanced the reliability of the SES-CD but did not similarly affect the CDEIS or GELS. As the SES-CD is more likely to be reliable than the CDEIS and can be optimised with targeted training, it is the preferred instrument for use in clinical trials.
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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 | 0.349 | 0.493 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".