Juvenile Atypical Ductal Hyperplasia: A Case Report
Bibliographic record
Abstract
Breast masses, benign and malignant, are extremely common in women. Benign breast masses have a variety of risk factors and are usually most common in women aged 30 - 40 years. Although much research has been conducted on benign breast disease and breast cancer in adult women, there remains a paucity of data on breast masses in adolescent women. More specifically, there is very little evidence regarding atypical ductal hyperplasia (ADH) in young women and the future risks it may carry as it is known to have a 4 to 5-fold increase for breast cancer in the adult population. We present a case of an 18-year-old female with ADH involving a fibroadenoma, with the hopes of highlighting the unique concerns and questions this diagnosis may bring to the young female population. Our patient’s questions centered around the risk this diagnosis carries of future cancer and what steps should be taken to minimize that risk. Although many models and guidelines exist to answer these questions in the older female, there is no consensus about treatment and monitoring ADH in a juvenile. By reviewing this case, we emphasize the need for future studies to quantify the risk of cancer progression from ADH in young females. We also demonstrate the need for guidelines to monitor and treat these findings in our younger populations. J Curr Surg. 2022;12(1):21-23 doi: https://doi.org/10.14740/jcs453
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| 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".