P281 Endoscopic healing assessed by advanced optical enhancement techniques combined with faecal calprotectin (FCP) can accurately assess histological healing in ulcerative colitis patients
Bibliographic record
Abstract
Mucosal healing (MH) is considered a key target of therapy in ulcerative colitis (UC) but there is debate about endoscopic healing, histological healing, and surrogate marker of MH using faecal calprotectin (FC). We have recently described and validated endoscopic MH using high-definition electronic chromoendoscopy. In this study, we aimed to investigate MH using multiple endoscopic scorings, FC, and validated histological scores. We prospectively obtained clinical data, endoscopic scores [Mayo Endoscopic Score (MES), Ulcerative Colitis Endoscopic Index of Severity (UCEIS) PICaSSO score (Paddington International virtual ChromoendoScopy ScOre)] and FC for UC patients undergoing colonoscopy using high-definition (Pentax) iScan optical enhancement (OE) or NBI near focus (Olympus). Histological scorings were assessed using Robarts Histological Index (RHI) and Nancy index (NI). Receiver-operating characteristics (ROC) curves were plotted to determine operating characteristics of FC alone or in combination with endoscopic scores to predict histological healing. In total, 44 patients (mean age 45 years, 52% men) were included. By partial Mayo score <2, 30 patients (68.2%) were in remission; however, endoscopic remission was seen in only 21(47.7%) with MES = 0 and UCEIS ≤ 1 and 18 (40.9%) with PICaSSO ≤ 2. The mean ± sd of FC was 465.5 ± 703.3 μg/g and 20 (45.5%) patients had FC ≤ 100 μg/g. The histological healing, defined as RHI ≤ 6 was seen in 21 (47.7%) patients and NI ≤ 1 was seen in 19 (43.2%). The threshold for FC alone as a predictor of histological healing using RHI was 313 μg/g with an accuracy of 84.1% (95% CI 69.9–93.4%) and AUROC of 87% (95% CI 75–98%), whilst for NI it was 112 μg/g, with accuracy of 81.8% (95% CI 67.3–91.8%) and AUROC 85% (95% CI 73–96%). The accuracy of predicting histological healing using a combination of PICaSSO and FC(≤100 μg/g) is 93.2% (95% CI 81.3–98.6%) with AUROC 96% (95% CI 91–100%) for both RHI and NI (formula used for NI=FC+1.5*Picasso). The combination of UCEIS and FC (≤100 μg/g) had an accuracy of 90.9% (95% CI 78.3–97.5%) in predicting histological healing for both RHI and NI, with an AUROC of 95% (95% CI 89–100%) and 94% (95% CI 87–100%), respectively. MES was not modelled in combination with FC as the best 2 endoscopy scores were modelled further. Abstract P281 – ROC curves predicting histological healing. The combination of PICaSSO and FC could help to identify UC patients with histological healing accurately than FC alone. PICaSSO with FC had better operating characteristics for prediction of histological healing than UCEIS and FC when using advanced endoscopic imaging with either iscan OE or NBI near Focus.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".