P275 Development and reliability of the new endoscopic virtual chromoendoscopy score: the PICaSSO score (the Paddington International Virtual ChromoendoScopy ScOre) in ulcerative colitis
Bibliographic record
Abstract
Background: Endoscopic inflammation and healing are important therapeutic endpoints in ulcerative colitis (UC). We developed and validated a new electronic virtual chromoendoscopy (EVC) score which could reflect the full spectrum of mucosal and vascular changes including mucosal healing in UC. Methods: Eight participants reviewed a 60-minute training module outlining the three different i-scan modes demonstrating the entire spectrum of inflammatory mucosal and vascular changes in UC. Performance characteristics in endoscopic scoring and predicting the histologic inflammation with EVC (iscan) by using 20 video clips before (pre-test) and after (post-test) were evaluated. Exploratory univariate factor analysis was performed on “PICaSSO” score covariates for mucosal and vascular score separately. Subsequently a proportional odds logistic regression model for the prediction of histological scores were analysed Results: The inter-observer agreement for Mayo endoscopic score in the pre-test (k=0.85, 95% CI: 0.78–0.90) and the post test (k=0.85,95% CI: 0.77–0.90) evaluation were very good. This was also true for UCEIS in the pre and post-test score inter-observer agreement (k=0.86,95% CI: 0.77–0.92 and k=0.84, 95% 0.75–0.91). The inter-observer agreement of the PICaSSO endoscopic score was very good in the pre and post-test evaluations (k=0.92, 95% CI: 0.87–96; k=0.89, 95% CI: 0.84–0.94). The accuracy of the overall PICaSSO score in assessing histological abnormalities and inflammation by Harpaz score was 57% (95% CI: 48–65%), by RHI 72% (95% CI: 64–79%) and by ECAP (full spectrum of histologic changes) 83% (95% CI: 76–88%). Conclusions: The EVC score “PICaSSO” showed very good inter-observer agreement. The new EVC score may be used to define the endoscopic findings of the mucosal and vascular healing in UC and reflected the full spectrum of histological changes.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".