Enhancing Enrollment in Acute Stroke Trials: Current State and Consensus Recommendations
Bibliographic record
Abstract
The Stroke Treatment Academic Industry Roundtable (STAIR) convened a session and workshop regarding enrollment in acute stroke trials during the STAIR XII meeting on March 22, 2023. This forum brought together stroke physicians and researchers, members of the National Institute of Neurological Disorders and Stroke, industry representatives, and members of the US Food and Drug Administration to discuss the current status and opportunities for improving enrollment in acute stroke trials. The workshop identified the most relevant issues impacting enrollment in acute stroke trials and addressed potential action items for each. Focus areas included emergency consent in the United States and other countries; careful consideration of eligibility criteria to maximize enrollment and representativeness; investigator, study coordinator, and pharmacist availability outside of business hours; trial enthusiasm/equipoise; site start-up including contractual issues; site champions; incorporation of study procedures into standard workflow as much as possible; centralized enrollment at remote sites by study teams using telemedicine; global trials; and coenrollment in trials when feasible. In conclusion, enrollment of participants is the lifeblood of acute stroke trials and is the rate-limiting step for testing an exciting array of new approaches to improve patient outcomes. In particular, efforts should be undertaken to broaden the medical community's understanding and implementation of emergency consent procedures and to adopt designs and processes that are easily incorporated into standard workflow and that improve trials' efficiencies and execution. Research and actions to improve enrollment in ongoing and future trials will improve stroke outcomes more broadly than any single therapy under consideration.
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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 | 0.450 | 0.432 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.016 | 0.014 |
| Research integrity | 0.024 | 0.032 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".