Insulin: A pacesetter for the shape of modern biomedical science and the Nobel Prize
Bibliographic record
Abstract
BACKGROUND: The 100th anniversary of the discovery of insulin in Toronto in 1921 is an important moment in medical and scientific history. The demonstration that an extract of dog pancreas reproducibly lowered blood glucose, initially in diabetic dogs and then in humans with type 1 diabetes, was a medical breakthrough that changed the course of what was until then a largely fatal disease. The discovery of the "activity", soon named "insulin", was widely celebrated, garnering a Nobel Prize for Banting and McLeod in 1923. Over the ensuing 100 years, research on insulin has advanced on many fronts, producing insights that have transformed our understanding of diabetes and our approach to its treatment. SCOPE OF REVIEW: This paper will review research on insulin that had another consequence of far broader scientific significance, by serving as a pacesetter and catalyst to bioscience research across many fields. Some of this was directly insulin-related and was also recognized by the Nobel Prize. Equally important, however, was research stimulated by the discovery of insulin that has profoundly influenced biomedical research, sometimes also recognized by the Nobel Prize and sometimes without this recognition. MAJOR CONCLUSIONS: By reviewing some of the most notable examples of both insulin-related and insulin-stimulated research, it becomes apparent that insulin had an enormous and frequently under-appreciated impact on the course of modern bioscience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".