Albert C. Broders, tumor grading, and the origin of the long road to personalized cancer care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it