What Is Known about Breast Cancer in Young Women?
Bibliographic record
Abstract
Breast cancer (BC) is the second leading cause of cancer-related death in women under the age of 40 years worldwide. In addition, the incidence of breast cancer in young women (BCYW) has been rising. Young women are not the focus of screening programs and BC in younger women tends to be diagnosed in more advanced stages. Such patients have worse clinical outcomes and treatment complications compared to older patients. BCYW has been associated with distinct tumour biology that confers a worse prognosis, including poor tumour differentiation, increased Ki-67 expression, and more hormone-receptor negative tumours compared to women >50 years of age. Pathogenic variants in cancer predisposition genes such as BRCA1/2 are more common in early-onset BC compared to late-onset BC. Despite all these differences, BCYW remains poorly understood with a gap in research regarding the risk factors, diagnosis, prognosis, and treatment. Age-specific clinical characteristics or outcomes data for young women are lacking, and most of the standard treatments used in this subpopulation currently are derived from older patients. More age-specific clinical data and treatment options are required. In this review, we discuss the epidemiology, clinicopathologic characteristics, outcomes, treatments, and special considerations of breast cancer in young women. We also underline future directions and highlight areas that require more attention in future studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".