Diabetes mellitus and pernicious anemia: interrelated therapeutic triumphs discovered shortly after William Osler’s death
Bibliographic record
Abstract
William Osler died on December 29, 1919, at the age of 70. Less than 1 year later, Frederick Grant Banting began a research project at the University of Toronto to find a treatment for diabetes mellitus. John James Rickard Macleod, director of physiology, gave him space, funding, and supplies. Charles Herbert Best, an undergraduate medical student, joined Banting. In 1921, Banting and Best isolated and purified insulin from pancreatic extracts of dogs. James Bertram Collip, a biochemist, helped in the purification process. The first American patient was treated with Toronto insulin in May 1922. Banting and Macleod were awarded the Nobel Prize in 1923 "for the discovery of insulin." George Richards Minot, a young hematologist in Boston, had an obsessive interest in the effect of diet on anemia. In October 1921, Minot developed weight loss and was diagnosed with severe diabetes mellitus. By January 1923, the pioneering diabetologist, Elliott Proctor Joslin, began to treat Minot with insulin. Minot's condition improved and he returned to work. In 1926, Minot and William Parry Murphy amazed the medical world when they eradicated anemia in 45 pernicious anemia patients by feeding them a half-pound of beef liver daily. Minot shared the 1934 Nobel Prize with Murphy and George Hoyt Whipple "for their discoveries concerning liver therapy in cases of anemia." Minot remained on insulin the rest of his life. Osler described the clinical findings and blood picture of pernicious anemia nearly a half century before Minot but his observations were largely ignored. Osler had an intriguing connection to Banting. Had he lived, Osler would have been ecstatic over these two monumental therapeutic breakthroughs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".