Multidimensional Phase I Dose Ranging Trials for Stroke Recovery Interventions: Key Challenges and How to Address Them
Bibliographic record
Abstract
Despite an increase in the amount of published stroke recovery research, interventions have failed to markedly affect the trajectory of recovery poststroke. We argue that early-phase research to systematically investigate dose is an important contributor to advance the science underpinning stroke recovery. In this article, we aim to ( a) define the problem of insufficient use of a systematic approach to early-phase, multidimensional dose articulation research and ( b) propose a solution that applies this approach to design a multidimensional phase I trial to identify the maximum tolerated dose (MTD). We put forward a design template as a decision support tool to increase knowledge of how to develop a phase I dose-ranging trial for nonpharmaceutical stroke recovery interventions. This solution has the potential to advance the development of efficacious stroke recovery interventions, which include activity-based rehabilitation interventions.
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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 | 0.505 | 0.626 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".