Network Meta‐Analysis: Comparison of Endoscopic Dysplasia Detection Technologies in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Novel colorectal cancer endoscopic surveillance techniques for inflammatory bowel disease (IBD) have recently been developed. AIMS: Compare the efficacy of currently available techniques for dysplasia detection in colonic IBD. METHODS: We conducted a systematic literature search from inception to March 2024 for randomized controlled trials (RCTs) or prospective cohort studies enrolling adults with IBD and having surveillance colonoscopy for dysplasia screening. Primary outcome was the number of dysplastic lesions (per-lesion analysis). Secondary outcome was the number of patients with dysplasia (per-patient analysis). We assessed endpoints using the frequentist NMA random effect model. RESULTS: We included 25 studies (22 RCTs). 4837 patients met eligibility criteria (850 total dysplastic lesions; 105 with advanced dysplasia). Nine different screening techniques were studied. In per-lesion analysis, dye-based chromoendoscopy (DCE) ranked the highest (83%) per SUCRA ranking. DCE was superior to HD-WLE (OR, 1.78; 95% CI, 1.06-3.00). There were no significant differences between NBI and DCE, HD-WLE with SR or CEM in head-to-head comparisons. In a sub-analysis confined to ulcerative colitis (UC), DCE ranked highest (98%) with per-lesion analysis, and was superior to NBI (OR, 1.69; 95% CI, 1.03-2.77). CONCLUSIONS: HD-WLE-SR, DCE and CEM demonstrated superiority over other techniques for detection of dysplasia in colonic IBD. DCE was superior for dysplasia detection in colonic IBD. DCE was superior to HD-WLE in colonic IBD. DCE was the best technique in UC. Further studies to compare HD-WLE-SR and NBI with DCE are warranted to ascertain performance equivalency and define the optimal surveillance technique.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.042 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".