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.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".