The Birth of the Subspecialty of Medical Oncology and Examples of Its Early Scientific Foundations
Bibliographic record
Abstract
"Passion is not accepting defeat."--Emil Frei III. In the early 1950s, an experimental and clinical program characterized by unique cross-fertilization was developed. The clinical importance of experimental animal models in drug screening and in establishing key chemotherapy concepts and the role of the pioneers of medical oncology in the design of the various phases of drug trials, using childhood acute leukemia and breast cancer as models, are discussed. Over a short time and with only a few drugs, principles of chemotherapy were laid out, which led to cures in such diseases as childhood acute leukemia and Hodgkin's disease and to improved disease-free survival in breast cancer. It is these and other achievements that paved the way to medical oncology. At the instigation of the American Society of Clinical Oncology (ASCO), the American Board of Internal Medicine made inquiries about a subspecialty in oncology. ASCO and B. J. Kennedy, MD, played key roles in the events leading to the official recognition of medical oncology as a new subspecialty of internal medicine in 1972.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".