Practical Considerations in Breast Papillary Lesions: A Review of the Literature
Bibliographic record
Abstract
CONTEXT: -Diagnosis of papillary breast lesions, especially in core biopsies, is challenging for most pathologists, and these lesions pose problems for patient management. Distinction between benign, premalignant, and malignant components of papillary lesions is challenging, and the diagnosis of invasion is problematic in lesions that have circumscribed margins. Obtaining a balance between overtreatment and undertreatment of these lesions is also challenging. OBJECTIVES: -To provide a classification and a description of the histologic and immunohistochemical features and the differential diagnosis of papillary breast lesions, to provide an update on the molecular pathology of papillary breast lesions, and to discuss the recommendations for further investigation and management of papillary breast lesions. This review provides a concise description of the histologic and immunohistochemical features of the different papillary lesions of the breast. DATA SOURCES: -The standard pathology text books on breast pathology and literature on papillary breast lesions were reviewed with the assistance of the PubMed database ( http://www.ncbi.nlm.nih.gov/pubmed ). CONCLUSIONS: -Knowledge of the clinical presentation, histology, immunoprofile, and behavior of papillary breast lesions will assist pathologists with the diagnosis and optimal management of patients with papillary breast lesions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".