Does confirmatory tumor biopsy alter the management of breast cancer patients with distant metastases?
Bibliographic record
Abstract
BACKGROUND: Decisions about systemic treatment of women with metastatic breast cancer are often based on estrogen receptor (ER), progesterone receptor (PgR), and Her2 status of the primary tumor. This study prospectively investigated concordance in receptor status between primary tumor and distant metastases and assessed the impact of any discordance on patient management. MATERIALS AND METHODS: Biopsies of suspected metastatic lesions were obtained from patients and analyzed for ER/PgR and Her2. Receptor status was compared for metastases and primary tumors. Questionnaires were completed by the oncologist before and after biopsy to determine whether the biopsy results changed the treatment plan. RESULTS: Forty women were enrolled; 35 of them underwent biopsy, yielding 29 samples sufficient for analysis; 3/29 biopsies (10%) showed benign disease. Changes in hormone receptor status were observed in 40% (P = 0.003) and in Her2 status in 8% of women. Biopsy results led to a change of management in 20% of patients (P = 0.002). CONCLUSIONS: This prospective study demonstrates the presence of substantial discordance in receptor status between primary tumor and metastases, which led to altered management in 20% of cases. Tissue confirmation should be considered in patients with clinical or radiological suspicion of metastatic recurrence.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".