Incorporating Biology Into Breast Cancer Staging: American Joint Committee on Cancer, Eighth Edition, Revisions and Beyond
Bibliographic record
Abstract
Higher-quality imaging, refined surgical procedures, enhanced pathologic evaluation, and improved understanding of the impact of tumor biology on treatment and prognosis have necessitated revisions of the AJCC breast cancer staging system. The eighth edition includes clinical and pathologic prognostic stages that incorporate biologic variables-grade, estrogen and progesterone receptor status, HER2 status, and multigene panels-with the anatomic extent of disease defined by tumor, node, and metastasis categories. The prognostic staging systems facilitate more refined stratification with respect to survival than anatomic stage alone. Because the prognostic staging systems are dependent on biologic factors, accuracy is dependent on rigorous pathologic evaluation of tumors and on administration of treatment dictated by tumor biology. It is anticipated that technological advances will facilitate even more refined determination of underlying biology within tumors and in the peripheral blood, which increasingly is being evaluated as a compartment that reflects the primary tumor and sites of distant metastases. Diseases should be staged according to the eighth edition staging system to accurately reflect prognosis and to allow standardized data collection. Such standardization will facilitate assessment of the impact of advances in diagnosis and treatment of patients with breast cancer.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".