A Hundred Years of Insulin Innovation: When Science Meets Technology
Bibliographic record
Abstract
The story of insulin development is a remarkable example of what can be accomplished in medicine when academic science meets translational biotechnology. The foundation was laid 100 years ago this year with the discovery of insulin in Toronto in 1921, prompting a Nobel Prize in 1923. The discovery made by Banting and Best (1) was so spectacular and of such importance for many people that it immediately led to collaboration with industry partners, most notably Eli Lilly in the U.S. and what became Novo Nordisk in Europe. One of the founding fathers of Novo Nordisk, Dr. August Krogh, was himself a Nobel Prize laureate. Krogh understood the scientific significance of the insulin discovery and partnered with people skilled in medicine as well as in technological development and upscaling. In the following decades, academic discoveries such as insulin crystallization (2) and binding of zinc (3) and protamine (4) immediately translated to benefits on insulin product characteristics that are of importance even today. Seminal discoveries in academia in the 1950s and 1960s unraveled the amino acid sequence as well as the secondary, tertiary, and quaternary insulin structures. These academic endeavors were led by Nobel Laureates Sanger (5) and Hodgkin (6), respectively. …
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.021 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".