Histologic Assessment in Ulcerative Colitis: A Survey of Pathologists’ Practices and Perspectives
Bibliographic record
Abstract
Background: Histologic remission is increasingly recognized as an important endpoint in ulcerative colitis (UC) management. Consensus guidelines on adopting histologic scoring systems in clinical practice are lacking in the United States. This study aimed to assess the knowledge, attitudes, and practices of pathologists, primarily located in North America, regarding histologic evaluation in UC. Methods: This study surveyed a group of pathologists who have completed postdoctoral medical training with demonstrated interest and involvement in the field of gastrointestinal pathology to evaluate their knowledge, practices, and perspectives on histologic assessment using standardized scoring systems in clinical practice in UC patients. The survey was hosted on an online platform, and responses were recorded anonymously. Results: A total of 57 responses were included in the analysis. Nearly two-thirds of pathologists acknowledged a lack of familiarity with the criteria for histologic remission as defined by the Nancy Index (NI), Robarts Histopathology Index (RHI), and Geboes score (GS). A majority (37/57; 65%) of pathologists did not support routine inclusion of a histologic score in pathology reports. The remaining 20/57 (35%) pathologists advocated for the incorporation of a standardized index, with the NI favored by 10/20 (50%) followed by the GS (n = 3; 15%) and the IBD-Distribution, Chronicity and Activity score (n = 3; 15%). Nearly a half (27/57; 47%) of the respondents acknowledged a favorable role for artificial intelligence in this setting. Conclusions: The current survey highlights the need for collaborative efforts among pathologists, gastroenterologists, and professional societies to establish a consensus guideline for routine histologic assessment in UC. Additional guidance from professional societies and research are required to integrate artificial intelligence-driven approaches into routine clinical practice.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".