Synchronous Occurrence of Triple-Negative Breast Cancer and Malignant Melanoma
Bibliographic record
Abstract
In people with cancer, multiple primary malignant neoplasms (MPMNs) are not unusual, and they may be caused by risk factors such as genetics, viral infection, smoking, environmental factors, or treatment-related variables. The frequency of MPMNs occurring in the same or separate organ systems is between 2% and 17%. The 5-year breast cancer survivors have been found to have around 3.6% chance of acquiring another neoplasm. In this case report, we present a very rare simultaneous occurrence of two highly malignant tumors - triple-negative breast cancer and cutaneous melanoma. We performed genetic tests for determining the link between both neoplasms. The patient was treated in an adjuvant setting with chemotherapy and immunotherapy with pembrolizumab. According to epidemiological studies, for primary cutaneous melanoma following breast cancer, the standardized incidence ratio (SIR) varied from 1.03 to 4.10, while for primary breast carcinoma following cutaneous melanoma, it varied from 1.16 to 5.13. A number of risk factors have been proven to increase the risk of a second primary malignancy. This case highlights the importance of risk factor assessment and thorough primary workup of each patient. It emphasizes the need for a personalized approach when treating synchronous neoplasms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".