Heterogeneity in Histological Evaluation of Microscopic Colitis in Randomized Clinical Trials: An Umbrella Review
Bibliographic record
Abstract
BACKGROUND: The diagnosis of microscopic colitis (MC) is based on endoscopic biopsy with histological assessment. Histological outcomes (remission, response or improvement) are important treatment targets in clinical trials. Although a substantial body of research on MC has been published in recent years, no standardized criteria currently exist for its histological outcomes. We sought to review and summarize the histological evaluation of MC in published systematic reviews (SRs) assessing the efficacy of interventions and to examine the heterogeneity in histological evaluation among the randomized controlled trials (RCTs) included in those SRs. METHODS: We conducted an umbrella review (ie, an overview of systematic reviews) of published SRs. A literature search of the Cochrane Database of Systematic Reviews, MEDLINE, and Embase was performed up to May 2025. Definitions of histological evaluation and monitoring following interventions were extracted and summarized from the published SRs and the RCTs included within them. RESULTS: Fourteen SRs with meta-analyses that focused on interventions were included. Nineteen RCTs were included in these SRs. Of them, 12 fully published RCTs reported histological outcome data and met our inclusion criteria. The definitions for histological outcomes varied between RCTs but were generally based on reduction in lamina propria cellularity, intraepithelial lymphocytes, or collagen band thickness. CONCLUSIONS: This umbrella review highlights the heterogeneity in the definitions of histological outcomes in MC RCTs. The summarized evidence will support ongoing efforts to develop consensus definitions for histological outcomes in order to facilitate clinical trials of medical therapies for MC.
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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 | 0.220 | 0.535 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".