Expert recommendations to standardize transcriptomic analysis in inflammatory bowel disease clinical trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: Substantial methodological and reporting heterogeneity confounds the interpretation and generalizability of transcriptomic data for inflammatory bowel disease (IBD) studies. We aimed to develop recommendations to standardize transcriptomic research in clinical trials. METHODS: A 2-part study was undertaken. A systematic review identified reports of transcriptomic analyses utilizing samples from IBD clinical trials. Studies that used global RNA assay platforms were included. Data regarding study design, methodological approaches, and reporting of transcriptomic research were extracted. The systematic review results informed a modified Research and Development/University of California Los Angeles appropriateness methodology process and the development of survey statements focused on topics with substantial methodological heterogeneity. A panel of 16 IBD translational researchers and gastroenterologists rated the appropriateness of survey statements in 2 rounds. RESULTS: The systematic review identified 37 reports that included transcriptomic analyses of samples from IBD patients. The appropriateness of 416 statements was rated by 15 panellists in the first survey. The final survey included 305 statements, of which 14 panellists rated 75% appropriate, 1% inappropriate, and 24% uncertain. The panel determined that transcriptomic analysis for multiple research objectives was appropriate at most phases of clinical development in patients with active disease. Recommendations regarding study sample size; biopsy number, location, preservation, and storage; and data analysis and reporting were also generated. CONCLUSION: The persistence of existing methodologic heterogeneity may continue to limit the value of transcriptomic research in IBD. This study provides expert recommendations to address and overcome these discrepancies and foster the inclusion of this research in clinical development.
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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 | 0.663 | 0.839 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.017 | 0.012 |
| Research integrity | 0.025 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".