O4 Multicentre prospective validation study of the paddington international virtual chromoendoscopy score (PICaSSO) in ulcerative colitis
Bibliographic record
Abstract
Introduction Mucosal healing (MH)is an important goal in the treatment of ulcerative colitis (UC). The newly published PICaSSO score characterises subtle mucosal and vascular changes and defines MH. 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&3 (Pentax, Japan) and inflammatory activity was assessed using Mayo, UCEIS and PICaSSO. Biopsies were taken for histological assessment using Robarts Histological Index (RHI), Nancy, ECAP, Geboes and Villanacci. Follow up data was obtained at 12 months. Results A total of 307 patients were recruited.The interobserver agreement for the PICaSSO score was 0.879 (95% CI 0.826–0.924). The PICaSSO total and PICaSSO mucosal scores strongly correlated with histology scores and was statistically better than MES and UCEIS as show in figure 1. When using a PICaSSO total score of ≤3 the AUROC to predict MH by RHI (≤3 + absence of neutrophils) was 0.90 (95% CI 0.86–0.94) and when we compare the AUROC of Picasso vs Mayo p was =0.06. When using the Nancy score (≤1) the AUROC was 0.816 (95% CI 0.77–0.87). A Kaplan-Meier curve shows a significant favourable survival probability without relapse with a PICASSO score of ≤3 Likelihood ratio test=26.41, p<0.0000. Conclusions This real-life validation study shows the electronic chromoendoscopy score, PICaSSO, 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".