Exploratory Clinical Pharmacology for Drug Repurposing: Feasible Approaches and Insights
Bibliographic record
Abstract
Join us for webinar from Prof. Ahmed El-Yazbi, Professor of Pharmacology and Therapeutics at Alamein International University, and Adjunct Professor of Chemistry and Biochemistry at Texas Tech University on the repurposing of drugs in clinical pharmacology. Prof. El-Yazbi graduated in 2001 from the Faculty of Pharmacy in Alexandria University, with a BPharm degree. He received his PhD in Pharmacology from the Faculty of Medicine at the University of Alberta in 2008. He was then awarded two fellowships from the Canadian Institutes for Health Research and Alberta Innovates-Health Solutions to conduct a research program studying the regulation of cerebral blood flow at the Hotchkiss Brain Institute at the University of Calgary. Dr. El-Yazbi has two board certifications in Clinical Pharmacy and Pharmacotherapy from Canadian and American Boards. Dr. El-Yazbi leads a large research group in Egypt and collaborates with multiple research partners nationally and internationally. His research uses multiple tools ranging from molecular studies to machine learning to focus on cardiometabolic pathophysiology, producing over 100 research articles and mentoring 25 post-graduate students. His translational research program investigates the mechanisms of cardiovascular dysfunction in metabolic disease and identifies targets for novel therapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".