P335 A prospective multicentre “real-life” international validation study of the PICaSSO endoscopic scoring system against histologic scoring system to define mucosal healing in ulcerative colitis
Bibliographic record
Abstract
Mucosal healing is an important therapeutic goal to achieve in patients with ulcerative colitis (UC). Recently, the Paddington International virtual ChromoendoScopy ScOre (PICaSSO)1 for UC has been developed and validated by international experts based on video assessment and showed precise assessment of subtle inflammatory mucosal and vascular changes and accurate differentiation between mild inflammation and mucosal healing. We now report the reliability and validation of the PICaSSO score in real life setting between international experts in a prospective multicentre study. Patients with different grades of UC inflammation were prospectively enrolled in six international centres (4–13 patients per centre) and underwent colonoscopy video recording with mucosal biopsies from the area imaged. A core of 48 high-quality videos were used in the study and stratified based on disease activity and scores by seven international participants. Mayo endoscopy score, UCEIS and PICaSSO scores were recorded by each expert participant. Histology was assessed for mucosal abnormalities and inflammation by using Robarts Histological Index (RHI). Inter- and intra-observer agreement was then calculated for PICaSSO for UC by kappa statistics. Paddington International virtual ChromoendoScopy ScOre The inter-observer agreement of the overall PICaSSO endoscopic score was good between the international raters (K = 0.66; 95% CI 0.54–0.71). The inter-observer agreement of the mucosal PICaSSO (K = 0.52; 95% CI 0.41–0.60 ) and the vascular PICaSSO (K = 0.55; 95% CI 0.43–0.65) was moderate. The accuracy of the overall PICaSSO in assessing histologic inflammation by RHI score was 84% (95% CI 0.77–0.88). The Spearman’s correlation coefficient of PICaSSO endoscopic score with RHI histological index was 61.3% (95% CI 55–67). The accuracy of PICaSSO overall score >1 to discriminate between Mayo endoscopic score 0 and Mayo endoscopic score 1 was 73.9% with a specificity of 91.2%. PICaSSO score in real-life multicentre setting in UC demonstrates good inter-observer agreement in a real life multicentre study and provides good correlation with the validated histology RHI. Given the ability to discriminate subtle endoscopic features, PICaSSO may be applied to discriminate between subtle/mild mucosal inflammation and mucosal healing, which is difficult with other endoscopic scoring systems such as Mayo Endoscopic Score and UCEIS. Iacucci et al. Gastrointest Endosc, 2017;86:1118–27.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".