DOP068 The virtual electronic chromoendoscopy score in ulcerative colitis exhibits very good inter-rater agreement in scoring mucosal and vascular changes after computerised module training: a study across academic and community practice
Bibliographic record
Abstract
Background: Mucosal healing is the desired therapeutic endpoint for clinical trials in ulcerative colitis (UC). However, conventional white light endoscopy may fall short of capturing the full spectrum of inflammatory change; and virtual electronic chromoendoscopy (VEC) can show ongoing disease activity even when Mayo scores suggest healing (Iacucci et al. Endoscopy 2015). Applicability of VEC scoring requires determination outside the expert setting; thus, our aim was to provide external validation among trainees, consultant gastroenterologists and colorectal surgeons, practicing across six general and specialist centres. Methods: 15 participants reviewed a computerised training module outlining HD and i-Scan modes. Anchor points for the VEC score indicated mucosal changes (crypt distortion, 0 [A–C]; microerosions, I [1–3]; erosions, II [1–3]; and ulceration, III [1–3]) and vascular alterations (non-dilated vessels, 0 [A–C]; dilated/crowded vessels, I [1–3]; mucosal bleeding, II [1–3]; and intraluminal bleeding, III [1–3]). Performance accuracy was tested using a video library pre-/post-training (n=30). Agreement between raters was tested for the Mayo score, UCEIS and VEC score, and results correlated with histology (New York Mount Sinai system; Harpaz et al.). Results: The inter-rater agreement was very good for the Mayo score, UCEIS scoring erosions/ulcers and overall, and for VEC scoring mucosal patterns in both modules (Table 1). For the vascular components of UCEIS agreement was only moderate, and did not improve post-training; unlike the agreement for VEC vascular patterns which improved significantly to very good. Correlation between histology and VEC score was highly significant for mucosal and vascular scoring (Spearman's ρ: 0.910, p<0.001; and 0.907, p<0.001; respectively, Figure 1). This was superior to the Mayo score (0.876, p<0.001) and UCEIS (0.887, p<0.001). Table 1. ICCs are from a two-way random model with absolute agreement, and are for single measures. All values are significant at a p<0.001. Figure 1. Correlation between the EVC score and histology in ulcerative colitis. Average score per video per participant for each category are presented (post-training). Conclusions: The VEC score demonstrates very good inter-observer agreement across all levels of experience and provides excellent correlation with histology. Unlike UCEIS, the VEC score does not have subjective elements (e.g. mucosal erythema, incidental/contact friability) and may better delineate vascular changes due to filter technology. Given the ability to define subtle endoscopic features, VEC may be applied to further stratify treatment paradigms for patients with UC.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".