Chromoendoscopy versus narrow band imaging in UC: a prospective randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Patients with long-standing UC have an increased risk for the development of colonic neoplastic lesions. Chromoendoscopy (CE) has been proven to enhance neoplasia detection while the role of virtual chromoendoscopy (VC) is still to be defined. OBJECTIVE: To compare the performance of CE to VC for the detection of neoplastic lesions in patients with long-standing UC. DESIGN: A multicentre prospective randomised controlled trial. 131 patients with long-standing UC were randomised between CE with methylene blue 0.1% (n=66) or VC with narrow band imaging (NBI) (n=65). Biopsies were taken from visible lesions and surrounding mucosa. No random biopsies were performed. The primary outcome was the difference in total number of neoplastic lesions detected in each group. RESULTS: There was no significant difference between NBI and CE for neoplasia detection. Mean number of neoplastic lesions per colonoscopy was 0.47 for CE and 0.32 for NBI (p=0.992). The neoplasia detection rate was not different between CE (21.2%) and NBI (21.5%) (OR 1.02 (95% CI 0.44 to 2.35, p=0.964). Biopsies from the surrounding mucosa yielded no diagnosis or dysplasia. The per lesion neoplasia detection was 17.4% for CE and 16.3% for NBI (OR 1.09 (95% CI 0.59 to 1.99, p=0.793). The total procedural time was on average 7 min shorter in the NBI group. CONCLUSION: CE and NBI do not differ significantly for detection of colitis-associated neoplasia. Given the longer withdrawal time for CE and easier applicability, NBI may possibly replace classical CE. TRIAL REGISTRATION NUMBER: NCT01882205; Results.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".