Bibliographic record
Abstract
In August of 2001, I was honored to serve as the Chairman of the 6 th Symposium of the World Artificial Organ, Immunology and Transplantation Society, held in Ottawa, Canada.During this conference, it was my great pleasure to organize a very special session entitled "Living Legends -Lessons Learned and Future Visions".This session brought together, in the same room, giants in our field who had pioneered organ transplantation, cardiovascular surgery, pacemakers, and artificial hearts among other major contributions.With the assistance of my co-chairman for the session, Dr.Howard Frazier (Texas Heart Institute, USA), a legend in his own right, we presented the first Living Legends Awards."In recognition of their achievement and excellent contributions to medicine", 12 living scientists, aged 60 and older, who have made major contributions to humanity through invention or discovery, and who had served as mentors to many in our field of endeavor "have been selected as a Living Legend".These Living Legends have contributed greatly to major scientific and technological advances in the 20 th Century through their work, teaching, and leadership.A brief summary of the contributions of each of the Living Legends Award recipients is outlined below in alphabetical order: Dr. Kazuhiko Atsumi (Tokyo University, Japan) led a team that took up the challenge of developing heart assist devices and artificial hearts in Japan.Dr. Atsumi has also been involved in research in the area of laser medicine and served as the President of Suzuka University of Medical Science and Technology.Dr. Wilfred Bigelow (Toronto General Hospital, Canada) discovered how to lower the body's oxygen requirements, through lowering the body's core temperature, allowing open-heart surgery to be performed safely in 1950.Dr. Bigelow also noted that regular electrical pluses restore normal cardiac rhythm leading to the development of the first external pacemaker for continuous clinical use.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".