Assessment of Endoscopic Healing by Using Advanced Technologies Reflects Histological Healing in Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Several studies have reported that ulcerative colitis [UC] patients with endoscopic mucosal healing may still have histological inflammation. We investigated the relationship between mucosal healing defined by modified PICaSSO [Paddington International Virtual ChromoendoScopy ScOre], Mayo Endoscopic Score [MES] and probe-based confocal laser endomicroscopy [pCLE] with histological indices in UC. METHODS: A prospective study enrolling 82 UC patients [male 66%] was conducted. High-definition colonoscopy was performed to evaluate the activity of the disease with MES assessed with High-Definition MES [HD-MES] and modified PICaSSO and targeted biopsies were taken; pCLE was then performed. Receiver operating characteristic [ROC] curves were plotted to determine the best thresholds for modified PICaSSO and pCLE scores that predicted histological healing according to the Robarts Histopathology Index [RHI] and ECAP 'Extension, Chronicity, Activity, Plus' histology score. RESULTS: A modified PICaSSO of ≤ 4 predicted histological healing at RHI ≤ 3, with sensitivity, specificity, accuracy and area under the ROC curve [AUROC] of 89.8%, 95.7%, 91.5% and 95.9% respectively. The sensitivity, specificity, accuracy and AUROC of HD-MES to predict histological healing by RHI were 81.4%, 95.7%, 85.4% and 92.1%, respectively. A pCLE ≤ 10 predicted histological healing with sensitivity of 94.9%, specificity of 91.3%, accuracy of 93.9% and AUROC of 96.5%. An ECAP of ≤ 10 was predicted by modified PICaSSO ≤ 4 with accuracy of 91.5% and AUROC of 95.9%. CONCLUSION: Histological healing by RHI and ECAP is accurately predicted by HD-MES and modified virtual electronic chromoendoscopy PICaSSO, endoscopic score; and the use of pCLE did not improve the accuracy any further.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".