OP26 The first real-life multicentre prospective validation study of the electronic chromoendoscopy score (Paddington International Virtual ChromoendoScopy ScOre) and its outcome in ulcerative colitis
Bibliographic record
Abstract
Abstract Background Mucosal healing is an important goal in the treatment of ulcerative colitis (UC). The newly published PICaSSO score characterises subtle mucosal and vascular changes and defines mucosal healing. We aimed to validate in real-life the PICaSSO score and assess its ability to predict relapse. Methods Patients with UC were prospectively recruited from 11 international centres. Participating endoscopists experienced in IBD received training on PICaSSO before starting the study. The rectum and sigmoid were examined using iScan 1,2 and 3 (Pentax, Japan) and inflammatory activity was assessed using UCEIS and PICaSSO. Biopsies were taken for the histological assessment using Robarts Histological Index (RHI) and Nancy. Follow-up was obtained at 12 months. Results A total of 278 patients were recruited (Table 1). The diagnostic performance in predicting histologic healing is shown in Table 2. When using PICaSSO score of ≤3 for mucosal and vascular architecture the AUROC to predict healing by RHI is 0.79 (95% CI 0.74–0.85) and 0.73 (95% CI 0.68–0.80) respectively and when using the Nancy score the AUROC is 0.78 (95% CI 0.72–0.84) and 0.77 (0.71–0.84). A total PICaSSO score of ≤8 and UCEIS score of ≤1 predicts remission at 12 months with an AUROC of 0.73 (0.65–0.80) and 0.71 (0.64–0.79). A Kaplan–Meier curve shows a favourable survival probability without relapse with a PICASSO score of ≤8 (Figure 1). Conclusion This real-life validation study shows the PICaSSO score can predict accurately histological healing and long-term remission and can be a useful tool in the management of 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".