Bibliographic record
Abstract
Writing this "editorial" has been delayed for 2 months of introspection, during which I attempted to analyze why the term "pioneer" might be applied to me.What follows is more a story of intoxicating personal fulfillment than that of planned scientific exploration.When Belding H. Scribner, in 1960, demonstrated that lost kidney function could be repeatedly replaced by Willem J. Kolff's life-sustaining artificial kidney, the disciplines of Nephrology and Artificial Organs were born.While it would make a better tale to recount how after thoughtful study, I sought nephrology training at the institution that established America's first acute renal failure hemodialysis unit and gained Nobel Prize recognition for successful kidney transplants in identical twins, my arrival after graduation from the State University of New York's Downstate Medical Center in 1957 at Boston's Peter Bent Brigham Hospital resulted from my medical chief's (General Perrin H. Long) telephone call to Brigham Medicine Chief George W. Thorn suggesting consideration of his aggressive student who might benefit from Brigham seasoning.Lacking knowledge of dialysis, transplants, or the concept of training at a Harvard institution, it had been my intent to pursue residency training as an internist at a local hospital.My first Brigham internship rotation was through its brand new artificial kidney service-a unique medicine training component then or now.After 2 weeks of daily duty, I was "hooked" by the combination of science fiction, futuristic medicine, and mystery.John P. Merrill, who returned from World War 2 duty as an Air Force Flight Surgeon to the Enola Gay-the Hiroshima B29 Bomber-directed what was termed the Cardiorenal Service and became my mentor.During my internship, Merrill filed my American Heart Association fellowship application, outlining testing of animal and human leukocyte antigenicity about which I knew less than nothing.Although I botched the fellowship interview, revealing my absolute ignorance of immunology, the fix was in and I got the award.To my surprise, the Brigham moved me from internship to fellowship without an intervening residency.Thereafter, it was a downhill roller coaster ride careening from transplants after total body radiation, thoracic duct leukocyte depletion, and application of chemotherapy to immunosuppression to blunt transplant rejection.I conversed daily with Joseph E. Murray (Nobel 1960) and was able to question Robert Schwartz about his use of 6-mercaptopurine to curb antibody synthesis in rabbits given bovine serum albumin while learning from Roy Y. Calne how BW 57-322 (azathioprine) permitted dogs to maintain renal allografts.During my first year of fellowship, Merrill's lab was visited by future Nobelists Medawar, Burnet, and Dausset, as well as multiple extraordinary founders of nephrology including Hamburger, Brun, Kincaid-Smith, Alwall, Relman, Richet, Schreiner, Seldin, Thurau, Kurokawa, and Barsoum.Without exaggeration, the intoxicating atmosphere of being where a disease (uremia) was being conquered was so seductive that I devoted nearly my total existence to immersion in Merrill's forward progress.Indeed, I was totally consumed by what is now called nephrology and never had the time to think of doing anything else.My late wife Mildred "Barry" (who
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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.020 | 0.042 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".