Can advanced endoscopic techniques for assessment of mucosal inflammation and healing approximate histology in inflammatory bowel disease?
Bibliographic record
Abstract
The targets of therapy in inflammatory bowel disease have transformed in the last few years. The standard definition of mucosal healing assessed using white light standard definition endoscopy is being challenged because even when endoscopy suggests mucosal healing, the presence of histological activity can often still be observed. Of note, microscopic signs of inflammation correlate with clinical outcomes such as risk of relapse, hospitalization and colorectal cancer. Therefore, histological healing has increasingly become an important target to achieve. Advanced endoscopic technologies have been developed and many are starting to be adopted in daily clinical practice. They can provide a more detailed view of the mucosal and vascular architecture almost at the histology level, including crypt, vessel architecture and cellular infiltration. So, these can provide a more accurate definition of mucosal and histological healing. In this review we focus on new advanced endoscopic techniques, and how these have the potential to reduce the gap between histological and mucosal healing.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".