Using Proliferative Markers and Oncotype Dx in Therapeutic Decision-Making for Breast Cancer: The B.C. Experience
Bibliographic record
Abstract
BACKGROUND: Proliferative scoring of breast tumours can guide treatment recommendations, particularly for estrogen receptor (er)-positive, her2-negative, T1-2, N0 disease. Our objectives were to □ estimate the proportion of such patients for whom proliferative indices [mitotic count (mc), Ki-67 immunostain, and Oncotype dx (Genomic Health, Redwood City, CA, U.S.A.) recurrence score (rs)] were obtained.□ compare the indices preferred by oncologists with the indices available to them.□ correlate Nottingham grade (ng) and its subcomponents with Oncotype dx.□ assess interobserver variation. METHODS: All of the er-positive, her2-negative, T1-2, N0 breast cancers diagnosed from 2007 to 2011 (n = 5110) were linked to a dataset of all provincial breast cancers with a rs. A 5% random sample of the 5110 cancers was reviewed to estimate the proportion that had a mc, Ki-67 index, and rs. Correlation coefficients were calculated for the rs with ng subcomponent scores. Interobserver variation in histologic grading between outside and central review pathology reports was assessed using a weighted kappa test. RESULTS: During 2007-2011, most cancers were histologically graded and assigned a mc; few had a Ki-67 index or rs. The ng and mc were significantly positively correlated with rs. The level of agreement in histologic scoring between outside and central pathology reports was good or very good. Very few cases with a low mc had a high rs (1.8%). CONCLUSIONS: Patients with low ng and mc scores are unlikely to have a high rs, and thus are less likely to benefit from chemotherapy. In the context of limited resources, that finding can guide clinicians about when a rs adds the most value.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".