Bridging the Gap Between Explanatory and Pragmatic Trials in Inflammatory Bowel Disease
Bibliographic record
Abstract
Randomized controlled trials (RCTs) are the gold standard for assessing the efficacy and safety of medical interventions. Randomized controlled trials conducted for registrational purposes in inflammatory bowel disease (IBD) face many unique challenges, one of which is their inability to capture the diverse and complex presentations of IBD seen in real-world settings. Highly restrictive eligibility criteria are a key contributor, as they aim to isolate treatment effects but by the very nature of their design create a “trial population” that may differ substantially from real-world patients.1,2 Such RCTs in IBD exclude types of patients that are routinely encountered in daily clinical practice, such as those with peri-anal disease, inflammatory strictures, proctitis, pouchitis, ostomies, dominant extraintestinal manifestations, multi-biologic exposure, or mild or severe disease presentations, resulting in homogeneous trial populations that fail to reflect the complexities encountered in routine clinical care.3,4 However, it is critical to recognize that pivotal RCTs designed for regulatory approval, are explanatory by design. Their primary focus is to demonstrate efficacy—not effectiveness—under highly controlled conditions in order to definitively establish benefits over placebo/active comparator if those differences truly exist, or to detect an excess of harms. Strict inclusion and exclusion criteria limit variability and control confounding factors, enhancing internal validity but often at the expense of external validity or generalizability.5 This is in contrast to pragmatic trials which are designed to test the benefit of interventions in routine clinical practice (testing effectiveness). While real-world evidence is valuable, it can never substitute for RCTs due to the inherent inability to balance comparative treatment groups on known or unknown confounders, meaning that causality cannot be reliably established.6 Thus, designing clinical trials that are more reflective of the populations we see in practice is one way of enhancing their external validity. This includes reducing bureaucratic barriers, such as overly rigid eligibility criteria, and implementing adaptive trial designs that maintain scientific rigor while improving inclusivity.6
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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 | 0.633 | 0.821 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".