Bibliographic record
Abstract
Lars Vistnes, M.D., Professor of Surgery, Emeritus at Stanford University, passed away March 28, 2016, at age 88 at the San Francisco home he shared with his wife Carol. His career as a plastic surgeon spanned many decades, and each decade saw Lars accepting new leadership challenges and responsibilities (Fig. 1).Fig. 1.: Lars Vistnes, M.D., 1927 to 2016. Used with permission from Carol Vistnes.Lars was a Canadian midwesterner who obtained his medical degree from the University of Manitoba. He completed an initial internship year at Winnipeg General Hospital, and then, after a period of community service in Canada, moved to San Francisco to complete a residency in general and plastic surgery at Saint Francis Memorial Hospital and St. Lukes Hospital under the leadership of Dr. Mar McGregor. His colleagues there included notables such as Mark Gorney and Ed Falces. Lars was recruited by then Stanford Department of Surgery Chair Robert A. Chase and Stanford Plastic Surgery Division Chief Don Laub to accept a full-time Stanford faculty position, as the first chief of plastic surgery at the Palo Alto Veterans Administration Hospital. Lars eventually became the chief of the Veterans Administration surgical service. His clinical interests included oculoplastic surgery and reconstruction of the anophthalmic orbit, and he achieved an international reputation in this field. Lars succeeded Don Laub as the chief of plastic surgery at Stanford. He was instrumental in the development of the Stanford Plastic Surgery Board Review Course and a strong proponent of international surgical volunteerism. Lars was very involved in the education of plastic surgery residents and in the promotion of plastic surgery as a specialty. He served as president of the California Society of Plastic Surgeons, and was chairman of the American Board of Plastic Surgery. He was the founding editor of the Annals of Plastic Surgery and served as Editor in Chief for the journal’s initial 10 years. He eventually became acting chair of the Department of Surgery at Stanford and was instrumental in the development of the “out of the box” concept of a Department of Functional Restoration. Although Lars had many retirement parties, he never really retired from Stanford. His final role was mentoring young Stanford faculty. Lars will be remembered for his dry Canadian humor, his absolute integrity, his strength of character in seeing family members through serious illness, and his stoical acceptance of the progressive loss of vision over the last years. Lars is survived by his lovely wife Carol, with whom he traveled the world; and sons Rick and Greg and son, Dean, himself a graduate of the Stanford plastic surgery program.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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; both teacher heads agree on what is shown here.
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".