Albert C. Broders, tumor grading, and the origin of the long road to personalized cancer care
Bibliographic record
Abstract
The roots of precision cancer therapy began at the Mayo Clinic in 1914 when surgical pathologist Albert C. Broders began collecting data showing that cancers of the same histologic type behaved differently. In March 1920, based upon 6 years of clinical follow-up, Broders published his first paper, utilizing data from over 500 cases of squamous cell carcinoma of the lip that he had blindly divided into four histologic grades based upon degree of differentiation, showing that numerical tumor "grading" allowed him to predict patient prognosis. Before this, surgeons had no scientific way to evaluate prognosis. Broders then replicated his work using other types of tumors at other body sites, as did several Mayo Fellows and pathologists at other institutions. Cuthbert Dukes in London, England not only replicated Broders' findings with rectal adenocarcinomas, he also used the same data to develop the first tumor "staging" methodology by focusing upon depth of local invasion and presence or absence of lymph node metastases. Soon, tumor grading, tumor staging, or the combination of both represented state-of-the-art prognostic techniques for scientific cancer care. This brief historical vignette celebrates the 100th anniversary of Broders' first paper, which is the starting point for the long road to personalized cancer care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".