P164 Mucosal healing (MH) assessed with PICaSSO (Paddington International Virtual ChromoendoScopy ScOre) and probe Confocal Laser Endomicroscopy (pCLE) do not reflect histological normalisation using the ECAP (Extent Chronicity Activity Plus) score
Bibliographic record
Abstract
Ulcerative colitis (UC) is a chronic disease that requires long-term therapy and the achievement of mucosal healing (MH) is the target of the treatment. The new histological score ECAP (Extent Chronicity Activity Plus) has been developed to detect minimal chronic changes. The electronic Virtual Chromoendoscopy Endoscopy (VCE) score PICaSSO (Paddington International Virtual ChromoendoScopy ScOre)1 and probe Confocal Laser Endomicroscopy (pCLE) scores reflect acute histological changes (Robarts Histological Index-[RHI]) well,2 but it is unknown whether these may reflect chronic histological changes. This is a prospective study involving 82 UC patients at the Endoscopy Unit, Foothills Medical Center, University of Calgary, Canada. For each patient, clinical data including follow-up up to 12 months, endoscopic scores (Mayo endoscopic score, PiCasso and pCLE score) and histological ECAP score were determined. The details of ECAP score has already been published.1 Receiver-operating characteristics (ROC) curves were plotted to estimate the cut-offs for PICaSSO and pCLE scores best predicting the MH determined by histological ECAP score. 70 patients (85.4%) were in clinical remission. We have compared the endoscopic scores (MES, PICaSSO, and pCLE) with histological healing defined by ECAP ≤ 4. Only 14 patients (28.6%) with Mayo 0 had ECAP ≤ 4. From the ROC curves, the best cut-off for PICaSSO score was 4, with sensitivity and specificity of 88.9% (95% CI 64.3%-98.6%) and 40.6% (95% CI 28.5%-53.6%), respectively with an area under ROC curve (AUROC) of 69.9% (95% CI 57.2%-82.6%). At this value, out of 54 patients with PICaSSO ≤ 4, only 16 (29.6%) have ECAP ≤ 4. The ROC curves for pCLE showed that the best cut-off point to detect MH (ECAP ≤ 4) was 11 with sensitivity of 94.4% (95% CI 72.7–99.9%) specificity of 31.3% (95% CI 20.2%-44.1%) with AUROC of 71.4% (95% CI 57.9%-84.8%). At this value of pCLE, 17 (27.9%) patient amongst 61 had ECAP ≤ 4. ROC curve for PICaSSO in the prediction of mucosal healing (ECAP ≤ 4) During the follow-up period, 8.06% of patients had a relapse: 80% had PICaSSO score ≤ 4, 80% had pCLE ≤ 11, and only one relapse 20% had ECAP ≤ 4. According our results, the endoscopic scores (PICaSSO and pCLE) are not able to predict histological healing calculated using ECAP (both chronic and acute changes) at the value ≤ 4. An ECAP score ≤4 may however predict stable mucosal healing with few relapses over 12 months. 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;86:1118–1127.e5. 2. Buda A, Hatem G, Neumann H, et al. Confocal laser endomicroscopy for prediction of disease relapse in ulcerative colitis: a pilot study. J Crohns Colitis 2014;8:304–11.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".