Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Neuroprotective drugs for acute stroke have appeared to work in animals, only to fail when tested in humans. With the failure of so many clinical trials, the future of neuroprotective drug development is in jeopardy. Current hypotheses and methodologies must continue to be reevaluated, and new strategies need to be explored. Summary of Review- In part 1, we review key challenges and complexities in translational stroke research by focusing on the "disconnect" in the way that neuroprotective agents have traditionally been assessed in clinical trials compared with animal models. In preclinical studies, determination of neuroprotection has relied heavily on assessment of infarct volume measurements (instead of functional outcomes), short-term (instead of long-term) end points, transient (instead of permanent) ischemia models, short (instead of extended) time windows for drug administration, and protection of cerebral gray matter (instead of both gray and white matter). Clinical trials have often been limited by inappropriately long time windows, insufficient statistical power, insensitive outcome measures, inclusion of protocol violators, failure to target specific stroke subtypes, and failure to target the ischemic penumbra. In part 2, we explore new concepts in ischemic pathophysiology that should encourage us also to think beyond the hyperacute phase of ischemia and consider the design of trials that use multiagent therapy and exploit the capacity of the brain for neuroplasticity and repair. CONCLUSIONS: By recognizing the strengths and limitations of animal models of stroke and the shortcomings of previous clinical trials, we hope to move translational research forward for the development of new therapies for the acute and subacute stages after stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it