Advanced endoscopic techniques in the assessment of inflammatory bowel disease: new technology, new era
Bibliographic record
Abstract
Endoscopic assessment of inflammation and mucosal healing is crucial for appropriate management in IBD. Current definition of endoscopic mucosal healing has been derived using previous generation of standard white light endoscopes. New endoscopy technologies widely available provide much more detailed images of mucosal and vascular patterns. Novel endoscopic techniques with high definition image, optical and digital enhancement have enhanced the quality and fine details of vascular and mucosal pattern so that endoscopic images have started to reflect histological changes for lesions and inflammation/healing. These technologies can now define subtle inflammatory changes and increase detection and characterisation of colonic lesions in patients with IBD. The best endoscopic technique to detect dysplasia in IBD is still debated. Dye chromoendoscopy with targeted biopsies is considered by Surveillance for Colorectal Endoscopic Neoplasia Detection and Management in inflammatory Bowel Disease Patients: International Consensus Recommendations (SCENIC consensus the standard of care and recommended for adoption by gastroenterologists in practice. In future, it is possible that well-trained colonoscopists using high definition equipment with image enhancements may be able to obtain equivalent yield without pan-colonic dye spraying and characterise lesions. Finally, SCENIC introduced endoscopic resectability of some dysplastic colonic lesions-new techniques may now better characterise endoscopic resectability and limit the number of colectomies. In this review, we will provide a state-of-the-art opinion on the direction of technological advances in the assessment of IBD and how new concepts will refine clinical practice.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".