Rapid Extensive Recurrence of Triple Negative Breast Cancer: Are Both Therapy and Cancer Biology the Culprit?
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) comprises 17-20% of all breast cancers and is one of the most common breast cancers. The lack of therapy and failure of existing therapy has been a challenge for clinicians. Doxorubicin (DOX) is the first-line therapy, however, it has significant limitations. Rapid extensive recurrence with metastasis in any cancer has been a challenge for surgeons and medical oncologists. The challenge can be due to failure of therapy, drug resistance, or epigenetic changes. Here, we are discussing a stage I breast cancer patient, operated and treated with appropriate chemotherapy with complete response, which recurred in less than 8 months and metastasized to bone, liver and other organs. We are also presenting lab data of the IL-6 secretions on exposure to DOX in one of the most commonly used TNBC cell lines MDA-MB-231. Breast cancer cell line MDA-MB-231 upon exposure to DOX shows an increase in IL-6 levels more than the already elevated IL-6 levels. This might be a reason for early recurrence. We concluded that patients with TNBC might benefit from a standard DOX treatment regimen with an inflammation-blocking agent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".