Decentralized clinical trials are better for the participants and for the planet: the case study of a double-blind randomized controlled trial in Singapore (PROMOTE study)
Bibliographic record
Abstract
Introduction: Novel technologies have enabled the decentralization of many aspects of clinical trials, but little research has been done on the impact of these changes on the participant experience, trial operations, or the environment. Methods: A fully decentralized clinical trial conducted in Singapore is used as a case study to evaluate the operational outcomes, environmental impact (via life cycle assessment), and participants experience (qualitative interviews) of the decentralized model compared to a traditional study with in-person visits. Results: The decentralized study achieved high participant retention rates (97%) and high completion rates for clinical data, even for biological samples. Participants found the decentralized model to be convenient and safe, especially during the pandemic. Moreover, the decentralized model was found to be more environmentally friendly and less detrimental to human health compared to traditional face-to-face clinical trials, primarily by reducing participants' use of cars for site visits. Discussion: While this study focused on the environmental impact, it is important to consider other factors such as participant safety, convenience, and data quality when evaluating the suitability of a decentralized clinical trial approach. Careful planning of data flow, database structure, and data protection measures is essential. This study contributes to improving the environmental footprint of clinical trials. Environmental sustainability should be among the factors that are evaluated when selecting trial models. Decentralized and hybrid clinical trials offer efficiency, effectiveness, and environmental benefits. Further research and adoption of these approaches are encouraged.
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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 | 0.121 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".