P148 Reproducibility of PICaSSO score by using narrow banding images (NBI) to assess mucosal and histological healing in ulcerative colitis (UC) patients
Bibliographic record
Abstract
Abstract Background The endoscopic and histological healing are key therapeutic targets in ulcerative colitis(UC) patients. PICaSSO (Paddington International virtual ChromoendoScopy ScOre)1,2 is a new Virtual Chromoendoscopy Endoscopic (VCE) score to better define mucosal healing by mucosal and vascular features. Originally validated using iSCAN platform, the aim of this study was to evaluate the reproducibility of PICaSSO with NBI near focus platform and to assess if this could predict histological healing. Methods We prospectively studied 78 UC patients (mean age 43.4 years, 52.6% male) who underwent colonoscopy for colitis assessment or surveillance using NBI near focus (Olympus, Japan). Endoscopic activity was assessed by using ulcerative colitis Endoscopic Index of Severity (UCEIS) and PICaSSO; whilst histological activity was scored by the Robarts Histology Index (RHI). ROC curves were performed to evaluate sensitivity, specificity and accuracy of endoscopy scores to predict histological healing. Results Out of 78 patients, 47 (60.3%) were in clinical remission according to the partial Mayo score. 28(35.9%) and 32(41.0%) were in endoscopic remission according to UCEIS≤1 and PICaSSO≤3, respectively. The best cut-off of UCEIS to predict histological healing was less or equal to 1. Sensitivity, specificity and accuracy were 84.6% (95% CI 63.5, 96.4), 88.5% (95% CI 70.1, 97.8) and 87.2% (95% CI 75.6, 93.6), respectively. The Area Under the ROC curve (AUROC) was 93.3% (95% CI 88.2, 98.3). The best threshold of PICaSSO in the prediction of histological healing was less or equal to 3. PICaSSO ≤ 3 have sensitivity of 96.2% (95% CI 76.9, 100), specificity of 86.5% (95% CI 67.3, 96.2) and accuracy of 89.7% (95% CI 77.6, 96.2) to predict histological healing, estimated as RHI ≤ 3. The AUROC was 95.3% (95% CI 91.1, 99.5). Conclusion PICaSSO VCE score can be easily and accurately reproduced with NBI near focus platform and it has better operating characteristics than UCEIS to predict histological healing defined by RHI. Reference
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".