Advanced technology for assessment of endoscopic and histological activity in ulcerative colitis: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Advanced endoscopic technologies led to significant progress in the definition of endoscopic remission of ulcerative colitis (UC) and correlate better with histological changes, compared with standard endoscopy. However, while studies have assessed the diagnostic accuracy of endoscope technologies individually, there are currently limited data comparing between technologies. As such, the aim of this systematic review was to pool data from the existing literature and compare the correlations between endoscopy and histologic disease activity scores across endoscope technologies. Methods: We searched PubMed and Embase until February 2021 for eligible studies reporting the correlation between endoscopy and histology activity scores in UC. Studies were grouped by endoscope technology as standard-definition white light (SD-WLE), high-definition white light (HD-WLE) or electronic virtual chromoendoscopy (VCE) and comparisons made between these groups. Results: A total of N = 27 studies were identified, of which N = 12 were included in a meta-analysis of correlations between endoscopic and histological activity scores. Combining these studies identified considerable heterogeneity ( I2: 89–93%) and returned a pooled correlation coefficient ( ρ) for the SD-WLE group of 0.74, which did not differ significantly from HD-WLE ( ρ: 0.65, p = 0.521) or VCE ( ρ: 0.70, p = 0.801). In addition, N = 4 studies reported the accuracy of endoscopic activity scores on WLE and VCE to diagnose histological remission. Pooling these found significantly higher accuracy for VCE, compared with WLE [risk ratio: 1.13, 95% confidence interval (CI): 1.07–1.19, p < 0.001]. Conclusion: Activity scores assessed using endoscopy are strongly correlated with activity on histology regardless of endoscopic technology. VCE seems to be more accurate in predicting histological remission than WLE. However, given the heterogeneity between the included studies, head-to-head trials are warranted to confirm these findings.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".