Translating promising preclinical neuroprotective therapies to human stroke trials
Bibliographic record
Abstract
Stroke is the third leading cause of mortality and carries the greatest socioeconomic burden of disease in North America. Despite several promising therapies discovered in the preclinical setting, there have been no positive results in human stroke clinical trials to date. In this article, we review the potential causes for failure and discuss strategies that have been proposed to overcome the barrier to translation of stroke therapies. To improve the chance of success in future human stroke trials, we propose that therapies be tested in stroke models that closely resemble the human condition with molecular, imaging and functional outcomes that relate to outcomes utilized in clinical trials. These strategies include higher-order, old-world, nonhuman primate models of stroke with clinically relevant outcome measures. Although stroke neuroprotection has been looked upon pessimistically given the many failures in clinical trials to date, we propose that neuroprotection in humans is feasible and will be realized with rigorous translational science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".