P227 Defining faecal calprotectin thresholds to predict endoscopic and histological healing in ulcerative colitis (UC) by using advanced optical enhancement techniques
Bibliographic record
Abstract
Abstract Background Faecal calprotectin (FC) is the most common surrogate marker of mucosal healing (MH) in UC. A number of endoscopic and histologic scoring systems in UC have been developed for defining MH. We report the optimum FC thresholds for defining MH using all the assessment methods. Methods In a prospective study we collected all clinical, endoscopic and histologic data and FC from 76 UC patients (mean age 44.2y, 50.0% male) who attended endoscopy unit for colitis assessment or surveillance. Endoscopic scores were determined by the same endoscopist (MI) and included Mayo Endoscopic Score (MES), Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and PICaSSO (Paddington International virtual ChromoendoScopy ScOre). Histological activity was scored by the Robarts Histology Index (RHI) and Nancy Index by the same pathologist (DZ). Faecal calprotectin was assayed using Buhlmann faecal turbo test, particle enhanced turbidimetric immunoassay. ROC curves were performed to evaluate sensitivity, specificity and accuracy of the optimum cut-off of FC to predict endoscopic and histological healing. Results The best cut-off for FC to predict endoscopic healing calculated as Picasso≤3 was 161 μg/g with Area Under ROC curve (AUROC) of 85.3% (95% CI 76.2, 94.4). Sensitivity, specificity and accuracy were 87.9% (95% CI 57.6, 100), 76.7% (95% CI 53.5, 90.7) and 81.6% (95% CI 68.4, 89.5), respectively. While, the best threshold of FC to predict UCEIS≤1 was 148 μg/g with AUROC of 89.2 (95% CI 81.9, 96.5). Sensitivity was 93.5% (95% CI 50.5, 100), specificity 82.2% (95% CI 53.3, 91.1) and accuracy 86.8% (95% CI 69.7, 92.1). The best threshold for FC to predict MES equal to 0, was 112 μg/g, with AUROC of 89.6 μg/g, (95% CI 82.5, 96.7). Sensitivity, specificity and accuracy were 89.7%ww (95% CI 39.2, 100), 85.1% (95% CI 55.3, 93.6) and 86.9% (95% CI 68.4, 92.1), respectively. The best value of FC to predict histological healing with RHI≤3 was 112μg/g with AUROC of 88.0% (95% CI 80.6, 95.4). Sensitivity, specificity and accuracy were 88.5% (95% CI 53.8, 100), 80.0% (95% CI 62.0, 90.0) and 82.9% (95% CI 72.5, 89.5), respectively. When used Nancy≤1 FC cut-off to predict healing was 172 μg/g with AUROC of 87.1% (95% CI 78.6, 95.6). Sensitivity was 96.4% (95% CI 60.7, 100), specificity 72.9% (54.2, 85.4) and accuracy 81.6% (69.7, 89.5). Conclusion Advanced enhancement technologies can accurately define the level of FC to predict endoscopic and histological healing in UC. The optimum FC threshold for MH by PICaSSO and by Nancy was similar (161 and 172 μg/g respectively), while the FC threshold for mucosal healing by MES and by RHI was 112 μg/g. The FC threshold for determining MH in clinical practice should be lower than at least 200 μg/g.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".