Lessons From Managing the Breast Malignant Adenomyoepithelioma and the Discussion on Treatment Strategy
Bibliographic record
Abstract
This study set out to investigate the clinical diagnosis and treatment strategies for malignant breast adenomyoepithelioma (AME), thus increasing the clinical knowledge on such disease. Two patients with malignant breast AME in Beijing Friendship Hospital were selected for study. Here we report the diagnosis and treatment processes in terms of the failure experience and lessons and relate our findings to those in the literature. Malignant breast AME is inclined to affect the areola area. It is recommended to conduct simple mastectomy combined with sentinel lymph node dissection due to the low sensitivity of the preoperative imaging diagnosis and difficulty in the pathological diagnosis. Malignant breast AME features strong invasiveness and vulnerability to recurrence and metastasis. Therefore, the operative schemes and clinical treatment strategies should be formulated based on the comprehensive analyses of the physical signs, imageological examinations and pathology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".