Essential Contributions of Pathologists and Laboratory Physicians Leading to the Discovery of Insulin
Bibliographic record
Abstract
CONTEXT.—: Frederick Banting, Charles Best, J. Bertrand Collip, and J. J. R. Macleod contributed to the discovery of insulin in 1921-1922. Recent advances in anatomic pathology, experimental pathology, and clinical pathology were necessary for the research in Toronto, Ontario, Canada, to begin and to succeed. OBJECTIVE.—: To explore the role of pathology and laboratory medicine in laying the foundation for the discovery of insulin. DESIGN.—: Available primary and secondary historical sources were reviewed. RESULTS.—: During a 3-decade period, pathologists, through autopsy pathology and experimental animal studies, were able to provide solid evidence that the pancreatic islets were the source of the internal secretion responsible for proper carbohydrate metabolism. Banting, a surgeon with no previous research experience, read about these studies in a case report with an extensive literature review by pathologist Moses Barron; this piqued Banting's interest and caused him to approach Macleod, a Toronto physiology professor, with an idea that initiated the research. Advances in clinical laboratory medicine, which allowed them to measure blood glucose levels using small blood volumes, were critical to their success. CONCLUSIONS.—: By 1921-1922, the pieces necessary to solve the puzzle were available. The primary reason that the time was ripe for the discovery was the contributions of pathologists and laboratory physicians in the preceding 3 decades. As the 100th anniversary approaches, our profession can take pride in its important contributions to the discovery of insulin, which is broadly recognized as one of the most important medical research advances of the 20th century.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".