Insulin therapy in type 2 diabetes mellitus: history drives patient care toward a better future.
Bibliographic record
Abstract
"Insulin will never be a success in the treatment of diabetics without the aid of the general practitioner."(1) These words, written by Elliott P. Joslin, MD, in 1923, still ring true today. After the first successful insulin injection was administered on January 23, 1922, to Leonard Thompson, a 14-year-old patient with type 1 diabetes mellitus at Toronto General Hospital, it did not take long before a diabetes clinic using insulin treatment was set up.(2) Insulin was subsequently provided to physicians in the United States for clinical trials, although many US patients with diabetes mellitus presented themselves to Sir Frederick Banting, MD, co-discover of insulin, in Toronto for insulin treatment. Within 2 years, insulin was being manufactured by multiple pharmaceutical companies and was available commercially in both the United States and Canada.(2) Almost immediately, health care professionals and others identified the problem of who was going to care for all of these patients. Patients were arriving at diabetes clinics expecting to receive insulin, often overwhelming the few physicians who were educated on the care of diabetic patients. For this reason, the physicians and nurses at the New England Deaconess Hospital in Boston initiated a teaching program so that general practitioners could learn all aspects of the management of diabetes.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".