2015 Presidential Address: 75 Years of Battling Diabetes−Our Global Challenge
Bibliographic record
Abstract
This address was delivered by Samuel Dagogo-Jack, MD, President, Medicine & Science, of the American Diabetes Association (ADA), at the Association's 75th Scientific Sessions in Boston, MA, on 7 June 2015. Dr. Dagogo-Jack is a professor of medicine and the director of the Division of Endocrinology, Diabetes and Metabolism and the director of the Clinical Research Center at The University of Tennessee Health Science Center, Memphis, TN, where he holds the A.C. Mullins Endowed Chair in Translational Research. He has been an ADA volunteer since 1991 and has served on several national committees and chaired the Association's Research Grant Review Committee. At the local level, he has served on community leadership boards in St. Louis, MO, and Tennessee. A physician-scientist, Dr. Dagogo-Jack's current research focuses on the interaction of genetic and environmental factors in the prediction and prevention of prediabetes, diabetes, and diabetes complications. He is the principal investigator of the Pathobiology of Prediabetes in a Biracial Cohort (POP-ABC) study and also directs The University of Tennessee site for the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) and the Diabetes Prevention Program (DPP)/DPP Outcomes Study (DPPOS). Dr. Dagogo-Jack earned his medical and research doctorate degrees from the University of Ibadan College of Medicine in Nigeria, holds a master's of science from the University of Newcastle upon Tyne in England, and completed his postdoctoral fellowship training in metabolism at the Washington University School of Medicine in St. Louis in Missouri. A board-certified endocrinologist, Dr. Dagogo-Jack has been elected to the Association of American Physicians and is the 2015 recipient of the Banting Medal for Leadership from the ADA. The ADA and Diabetes Care thank Dr. Dagogo-Jack for his outstanding leadership and service to the Association.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.169 | 0.102 |
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".