Nested Randomized Controlled Trials in Large Databases: An Opportunity for Inflammatory Bowel Disease?
Bibliographic record
Abstract
INTRODUCTION: Although randomized controlled trials (RCTs) are the gold standard for investigating the efficacy and safety of interventions, they present major operational challenges due to their complexity, time-consuming nature, and costs. To address some of these difficulties, RCTs nested in cohorts (RCTsNC) have been developed. The aim was to review the opportunities and challenges of RCTsNC in inflammatory bowel disease (IBD). METHODS: A literature search was conducted using MEDLINE, Embase, Cochrane and Clinicaltrials.gov from inception until March 2024 to identify studies focusing on this topic. RESULTS: RCTsNC is an emerging trial design, which has been successfully utilized across several medical disciplines but not IBD. It enables the use of longer-term longitudinal data for safety and efficacy assessment, and enhanced recruitment and follow up processes. Observational data for IBD, derived from research (cohort and case-control studies) and non-research sources (electronic health records and registries), provides access to comprehensive records for a large number of IBD patients, which could present an opportunity to enhance the performance of RCTsNC. Leveraging pre-existing cohorts and their organizational structures improves patient acceptance and is more economical compared to traditional randomized trials. It may permit researchers to address knowledge gaps in IBD (specific sub-populations, or the effect of environmental exposures on disease course). Limitations of RCTsNC include the risk of selection bias and constraints related to comparisons with placebo. CONCLUSION: RCTsNC offers a promising opportunity for IBD research and provides an alternative study design given the challenges of conventional trial designs in the current IBD RCT landscape.
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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 | 0.594 | 0.802 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".