P0350 Asia Pacific Association of Gastroenterology consensus statements on histopathological evaluation of Inflammatory Bowel Diseases
Bibliographic record
Abstract
Abstract Background Mucosal histological activity is increasingly valued as a treatment endpoint in inflammatory bowel diseases (IBD). In the Asia Pacific region, however, the utility and acceptability of IBD histology as a treatment endpoint is uncertain due to the heterogeneity of IBD prevalence, resourcing, and level of knowledge among practitioners. There is an opportunity to engage clinicians to harmonise histology reporting and collaborate with pathologists in this field. To this end, we developed statements to determine their consensus through anonymous voting on histological features, processing, reporting, and relevance to treatment outcomes in ulcerative colitis and Crohn’s disease. Methods Representatives of the Asia Pacific Association of Gastroenterology, in collaboration with pathologists, voted anonymously in accordance with modified Delphi methodology on statements relevant to IBD and histology. Domains of interest were histological features of ulcerative colitis and Crohn’s disease, relevance to clinical management, and the potential utility of artificial intelligence in grading histological disease severity. Level of evidence and recommendation grade were included in accordance with the NHMRC guidelines of Australia. Results Consensus was reached on 37 statements concerning definitions, pathology processing and reporting, scoring system, and relevance to clinical outcomes (Figure). There was uncertainty on escalating treatment when histological remission is not achieved, preventing consensus on this statement. Knowledge gaps were identified over the role of artificial intelligence. Conclusion These consensus statements provide recommendations, with specific relevance to the Asia Pacific region, on the role of histology in IBD ways to harmonise its use. The consensus statements will promote understanding and applicability in research and in the routine management of IBD.
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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 | 0.166 | 0.269 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".