The challenges and pitfalls of diagnosing adenomyoepithelioma in needle core biopsies of the breast
Bibliographic record
Abstract
Adenomyoepithelioma (AME) is a rare mammary neoplasm characterized by a biphasic proliferation of both myoepithelial and epithelial cells. These two cellular populations can contribute disproportionately to the lesion and assume a wide spectrum of growth patterns and histopathological features. Further, large variations exist in clinical presentation and imaging findings. A clear challenge that therefore arises is identifying AME on limited core biopsy material. As there are numerous potential mimickers of AME depending on the sampled region, accurate diagnosis of the entity is crucial. Recognizing the dual nature of AME and the architectural features it can possess, in combination with a panel of immunohistochemical markers, is necessary to establishing a diagnosis. Herein, we present a comprehensive approach and guiding principles to reaching a definitive diagnosis of AME on breast needle core biopsies.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".