Breast Cancer with Internal Mammary Node Metastases: A Case Presented in a Tumor Board Session and Decision Making
Bibliographic record
Abstract
Background: There are several therapeutic options available for breast cancer treatment, now incorporating innovative targeted molecular therapies. Metastatic breast cancer is usually treated with chemotherapy and/or hormonotherapy. Surgery has not been shown to improve survival. Adjuvant radiotherapy (RT) has been proven to be effective in the treatment of locally advanced breast cancer, reducing locoregional recurrence. The optimal treatment of internal mammary lymph nodes (IMN) metastases remains controversial. Case presentation: A 48-year-old woman was diagnosed with invasive breast cancer with ipsilateral metastases to axillary lymph nodes and a contralateral IMN metastasis. This case was presented twice during the tumor board sessions of the Surgical Oncology Service at the Centre hospitalier de l'Université de Montréal (CHUM), Montréal, Canada. Question: Does the internal mammary chain (IMC) dissection could be used as a treatment approach in breast cancer with IMC metastasis? Conclusion: Internal mammary chain dissection should be discussed in tumor board sessions on a case-by-case basis. There are no strong guidelines on the management of IMN metastasis in breast cancer, but there is growing evidence that these women should be treated with curative intent.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".