Presentation of the 2006 Morris F. Collen Award to Edward H. (Ted) Shortliffe
Bibliographic record
Abstract
The American College of Medical Informatics is an honorary society established to recognize those who have made sustained contributions to the field. Its highest award, for lifetime achievement and contributions to the discipline now known more inclusively as biomedical informatics, is the Morris F. Collen Award. Dr. Collen's own efforts as a pioneer in the field stand as the embodiment of creativity, intellectual rigor, perseverance, and personal integrity. At most once a year, the College gives its highest recognition to an individual whose attainments have, throughout a career, substantially advanced the science and art of biomedical informatics. In 2006, the College was proud to present the Collen Award to Edward Hance Shortliffe, M.D., Ph.D. (Figure 1). As a physician, computer scientist, researcher, educator, and eloquent spokesperson for the field, Dr. Shortliffe's career contributions make him most deserving of the recognition embodied in the Collen Award. Figure 1 Edward H. Shortliffe, M.D., Ph.D. Edward “Ted” Shortliffe was born on August 28th, 1947, in Edmonton, Alberta, Canada (see Figure 2). Ted's father was a physician and hospital administrator and his mother was a high school English teacher. The family moved to Connecticut while Ted was a youngster (1954), and in 1962 he became a U.S. citizen. Figure 2 Ted Shortliffe, circa 1948. After graduating from high school and spending a year as an exchange student in Great Britain, Ted entered Harvard in the fall of 1966. As an undergraduate, he sought a research project in applied mathematics, which was the concentration at Harvard at the time that included computer science. An advisor steered Ted to the Laboratory of Computer Science (LCS) at Massachusetts General Hospital (MGH). The LCS, directed by Octo Barnett, would become Ted's first contact with the burgeoning field that would later become biomedical informatics. On that first visit, Ted was introduced to Bob …
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".