Methods for handling missing segments in Crohn’s disease clinical trials: analysis from the EXTEND trial
Bibliographic record
Abstract
Recently in Gut , Gottlieb et al summarised considerations for endoscopy central reading in IBD clinical trials.1 Achieving endoscopic remission is an important measure of therapeutic efficacy, and most Crohn’s disease (CD) trials now require video-recorded ileocolonoscopy at screening and for evaluation of the primary outcome. While the Crohn’s Disease Endoscopic Index of Severity (CDEIS)2 and the Simplified Endoscopic Score for Crohn’s Disease (SES-CD)3 are commonly used instruments, Gottlieb et al correctly highlight that these scores are sensitive to missing data if one or more of the five ileocolonic segments are not examined. Bowel segments may not be visualised if there is an impassable stricture, when the bowel preparation is poor or if there are technical challenges precluding procedure completion. In these situations, appropriately handling missing data is essential because the total endoscopic score may not be reflective of the actual disease burden. We empirically evaluated the effect of different methods for handling missing data from non-visualised segments on the SES-CD and CDEIS. Ileocolonoscopy videos from baseline and week 12 in the Extend the Safety and Efficacy of Adalimumab through Endoscopic Healing (EXTEND) trial were used.4 EXTEND was a randomised, placebo-controlled trial evaluating adalimumab in patients with moderate-to-severe CD. Six methods of handling missing segments were applied: 1. No imputation: non-visualised segments ignored. 2. Worst …
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 | 0.342 | 0.488 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".