Adaptive trials in stroke: Current use & future directions
Bibliographic record
Abstract
Abstract Inclusion of adaptive design features in a clinical trial provides pre-planned flexibility to dynamically modify a trial during its conduct, while preserving validity and integrity. Adaptive trials are needed to accelerate the conduct of more efficient, informative, and ethical clinical research in the field of neurology as compared to traditional fixed designs. Stroke is a natural candidate for adoption of these innovative approaches to trial design. This Research Methods in Neurology paper is informed by scoping review that identified 45 completed and ongoing adaptive clinical trials in stroke that were appraised: 14 trials had published results with or without a published protocol, 15 trials had a published protocol, and 16 trials were registered only. Treatments spanned acute (n=28), rehabilitation (n=8), prevention (n=8), and rehabilitation and prevention (n=1) domains. A subsample of these trials were selected to illustrate the utility of adaptive design features and discuss why each adaptive feature(s) were incorporated in the design to best achieve the aim, whether each individual feature was used and if it resulted in expected efficiencies, and any learnings during preparation, conduct or reporting. We then discuss the operational, ethical, and regulatory considerations that warrant careful consideration during adaptive trial planning and reflect on the workforce readiness to deliver adaptive trials in practice. We conclude that adaptive trials can be designed, funded, conducted, and published for a wide range of research questions and offer future directions to support adoption of adaptive trial designs in stroke and neurological research more broadly.
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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 | 0.483 | 0.616 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".