Design and Execution of Sustainable Decentralized Clinical Trials
Bibliographic record
Abstract
The decentralized clinical trial (DCT) approach is increasingly recognized as a means to accelerate the development of potential therapeutic interventions. DCTs have a crucial advantage over traditional clinical trials: patients are monitored in their environment using technology (e.g., wearables), that capture data as they continue in daily life. This narrative review outlines a gap analysis focused on the frameworks and guidance from expert working groups and regulatory agencies for the design and execution of DCTs. Eight DCT elements guided the analysis and summarized the frameworks and guidance: (1) suitability, (2) protocol, (3) investigational medicinal product (IMP) supply, (4) investigators and health care providers, (5) safety, (6) regulatory and ethics, (7) data and technology, and (8) engagement, communication, and advocacy. Based on the gap analysis, two key takeaways were identified: (1) a need for a comprehensive sustainability assessment of each DCT element; and (2) current frameworks and guidance provide recommendations on social sustainability and some on economic sustainability. DCTs are an essential evolution in healthcare research; however, more guidance related to a comprehensive assessment of designing and executing sustainable DCTs is needed. This is especially the case for environmental sustainability, including, for example, carbon footprint and disposal of IMPs and sensors.
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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 | 0.207 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".