Translational Medicine for Stroke Drug Discovery
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
Over the past 20 years, an estimated $1 billion has been spent in research and development of stroke therapeutics; however, this huge investment has failed to produce a clinically efficacious drug with the exception of the thrombolytic agent Activase (tPA). This sobering reality has been the subject of numerous reflections by renowned leaders in stroke research with special focus on the most recent failed clinical trials. The validity of the neuroprotection strategy has been questioned and efforts to substantially modify the quality of stroke research have been examined. The consistent failures of the pharmaceutical industry to develop a neuroprotective drug for ischemic stroke have had a major impact on the assessment of stroke as an attractive therapeutic area for drug discovery. Many pharmaceutical companies have scaled down their stroke programs, reflecting skepticism about the prospect of contemporary stroke drug discovery strategy based on neuroprotective agents. In this article, we present a Translational Medicine perspective on critical issues that the pharmaceutical industry and the academic community encounter but often ignore during stroke therapeutic development. This Translational Medicine framework offers a systematic analysis of the possible deficiencies that likely underwrote the colossal failure of clinical trials with neuroprotective drugs. In addition, we offer a biomarker-based system that aims at providing "proof of concept" along the discovery and development pipeline, which if implemented along early preclinical and clinical development phases, might significantly reduce risks and enable success.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it