Adaptive Platform Trials in Stroke
Bibliographic record
Abstract
Clinical trials of treatments for stroke have generally utilized 2-arm, randomized designs to evaluate a single intervention against a control. Running separate clinical trials, with each addressing a single therapeutic question, is resource intensive and slows evidence generation, especially in a field with rapidly expanding treatment options and evolving practices. Platform trials-randomized clinical trials designed to evaluate multiple interventions that may enter and exit the ongoing platform based on a master protocol-accelerate the investigation of multiple therapeutic options within a single infrastructure. This in turn has the potential to accelerate access to new interventions for patients with stroke that can save lives and improve outcomes. In the context of acute ischemic stroke, 2 new platform trials have been established, the STEP trial (StrokeNet Thrombectomy Endovascular Platform) and ACT-GLOBAL (A Multi-Factorial, Multi-Arm, Multi-Stage, Randomised, Global Adaptive Platform Trial for Stroke), to address multiple therapeutic questions simultaneously using a multifactorial design including Bayesian modeling and other adaptive features. These trials are designed to maximize the information obtained from each participant, to align clinical research more closely with the complexities of clinical care, and to accelerate the identification of effective therapies. This article explores conceptual, practical, and statistical considerations in the design and implementation of adaptive platform trials and highlights their potential to accelerate the identification of new therapies, management, and rehabilitation in stroke.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.132 | 0.306 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".