Can Molecular and Cellular Neuroprotection Be Translated Into Therapies for Patients?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The concept of neuroprotection is based on sound scientific data derived in preclinical studies. However, candidate neuroprotectants have never been successfully translated to patients. METHODS: A review of past approaches to cellular and molecular neuroprotection, preclinical neuroprotection studies, and clinical approaches was undertaken. RESULTS: Although there is no evidence for fundamental barriers in biological principles that limit the translation of promising therapies to humans, ample evidence exists as to a lack of rigor in preclinical studies, obstacles posed by the complexities of acute ischemic stroke syndromes, and regulatory barriers. Alternative methods to translating stroke drugs may require trials in restricted stroke indications in well-defined patient populations. CONCLUSIONS: The translational gap between cellular and molecular neuroscience and patient therapy may be bridged by first developing therapies for narrow stroke indications. A single success may stimulate further research, funding, and a capacity to generalize initial results to broader stroke populations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".