P254 Re-defining the concept of endoscopic and histological healing by using electronic virtual chromoendoscopy and probe confocal endomicroscopy in ulcerative colitis
Bibliographic record
Abstract
The treatment goal of UC has shifted from symptomatic remission alone to endoscopic and recently histological healing.1 The new validated Virtual Chromoendoscopy (VCE) score, PICaSSO (Paddington International virtual ChromoendoScopy ScOre)1 offering detailed mucosal and vascular assessment, and probe confocal laser endomicroscopy (pCLE) as real time in vivo histology, aimed to re-define the concept of mucosal healing (MH). We specifically explored the magnitude of difference between endoscopy and histology defined MH using refined endoscopic assessments. In total, 82 UC, 8 controls, male 65.6%; mean age 49.9, SD 14.8 were prospectively enrolled at endoscopy unit, University of Calgary. The endoscopic activity was evaluated by Mayo Endoscopic Score (MES) and PICaSSO mucosal and vascular pattern 1 and thereafter with pCLE (Cellvizio, Paris) after IV fluorescein. The pCLE findings were graded as (A) crypt architecture (Grades 1–4); (B) leakage of fluorescein (Grades 1–4); (C) vessel architecture (Grades 1–4); (D) blood flow (Grades 1–4). Histological score (Robarts histological index, RHI) was used to score histological inflammation. Receiver-operating Characteristic (ROC) curves were plotted to calculate the best cut-off threshold of PICaSSO and pCLE scores to predict histological healing. For overall PICaSSO score, the optimum cut-off threshold for predicting histological healing defined as RHI ≤ 6 was 4, with sensitivity of 90.0% (95% CI 75.6–96.2) specificity 100% (95% CI 84.6–100), and accuracy of 92.7% (95% CI 84.8–97.3). The overall PICaSSO score of 4 or less was associated with all patients having an RHI ≤ 6. The best cut-off threshold for pCLE score was 10, with sensitivity 95.0% (95% CI 86.0%–99.0%), specificity 95.5% (95% CI 77.2–99.8) and accuracy of 95.1% (95% CI 88.0–98.7). The accuracy of predicting histological healing using PICaSSO or pCLE were superior to MES 0, which had sensitivity of 80% (95% CI 67.6–89.2), specificity 95.5% (95% CI 77.2–99.9), and accuracy of 84.2% (95% CI 74.4–91.3). ROC curve of PICaSSO and pCLE for predicting histological healing. The new VCE PICaSSO score and pCLE score can predict histological healing defined by RHI accurately. Advances in endoscopy enable close approximation to histology and can accurately re-define in real-time MH. Overall PICaSSO score of 4 or less was associated with RHI ≤ 6 in all patients. Large prospective studies are necessary to ascertain whether, with new endoscopic technologies such as readily available VCE, histology can still provide additional information about course of UC. References 1. Iacucci M, Daperno M, Lazarev M, et al. Development and reliability of the new endoscopic virtual chromoendoscopy score: the PICaSSO (Paddington International Virtual ChromoendoScopy ScOre) in ulcerative colitis. 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".