The Role of Adaptive and Innovative Trial Designs in Diabetes Research: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Adaptive and master protocol clinical trials offer significant advantages for diabetes research, including enhanced efficiency and personalized treatment strategies. PURPOSE: This scoping review aimed to systematically map the use of adaptive and master protocol designs in interventional trials for type 1 and type 2 diabetes, identify research gaps, and highlight opportunities for broader implementation. DATA SOURCES: A systematic literature search was performed using MEDLINE, Embase, CENTRAL, Emcare, Global Health, Web of Science, and clinical trial registries. Gray literature searches complemented database findings. STUDY SELECTION: Studies using adaptive, platform, basket, or umbrella trial designs in people with type 1 or type 2 diabetes were included. DATA EXTRACTION: Data were charted using a standardized form. Extracted variables included diabetes type, trial design, adaptive features, interventions, end points, and key findings. DATA SYNTHESIS: Of 396 articles screened, 6 published adaptive trials met the inclusion criteria: 3 in type 1 diabetes, 1 in type 2 diabetes, and 2 in diabetes-related neuropathy. Most used adaptive features for dose finding, response-adaptive randomization, and sample size reestimation. No published platform, basket, or umbrella trials were identified. Six ongoing adaptive trials in type 1 diabetes were identified through registry searches, four under an adaptive platform master protocol. LIMITATIONS: Despite a comprehensive search, some gray literature and unpublished studies may have been missed. Risk of bias was not assessed, consistent with scoping review methodology. CONCLUSIONS: Adaptive and master protocol trials remain rare in diabetes. Overcoming barriers through targeted training and awareness, robust regulatory frameworks, and strategic incentives could support broader adoption.
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 | 0.379 | 0.715 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".