Bench to Bedside Discovery, Innovation, Global Health Equity, and Security
Bibliographic record
Abstract
Dr Dzau was born in Shanghai. He received his Bachelor of Science in Biology and his MD degree from McGill University. He was a medical resident, Chief Resident, and the founding Chief of the Division of Vascular Medicine at the Peter Bent Brigham Hospital (now the Brigham and Women's Hospital). He moved to Stanford in 1990 as the Chief of the Division of Cardiovascular Medicine and later became Chairman of the Department of Medicine. Six years later, he returned to Harvard Medical School as the Hersey Professor of the Theory and Practice of Medicine and as Chairman of the Department of Medicine at Brigham and Women's Hospital. He then became the Chancellor for Health Affairs, President, and CEO of the Duke University Medical Center. In 2014, he was elected to become the President of the Institute of Medicine (now the National Academy of Medicine). He is a member of the National Academy of Medicine, the American Academy of Arts and Sciences, and the European Academy of Sciences and Arts.
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.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 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 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".