Routine Breast Cancer Screening in Average-Risk Women Younger Than 50 Years: Current Paradigms Based on National Guidelines
Bibliographic record
Abstract
Breast cancer poses a large health care burden. More than 270,000 women are diagnosed with breast cancer every year in the United States alone, and more than 40,000 women will die from the disease over the same period. Advances in routine screening and curative treatment options have led to mean 5-year survival rates for localized and regional disease of 98.9% and 85.7%, respectively. Diagnosis at an early stage, often due to routine screening, represents one of the most important prognostic factors for survival. Routine mammography screening in average-risk women 50 years and older has reduced the age-adjusted mortality rate from breast cancer by 34% just over the past 20 years.2,3 While there is consensus among national health organizations regarding the benefits of routine mammographic screening in women 50 years and older, screening recommendations for average-risk women aged between 40 and 49 years vary. Differences in screening recommendations among national organizations largely reflect variations in assessment of the benefit-to-harm ratio of screening women aged between 40 and 49 years who are less likely to develop breast cancer, compared with older women. Women who do develop breast cancer in this age group, however, are more likely to develop more aggressive disease.4,5 Over the past decade, this has become an increasingly important topic of discussion as breast cancer shifts to a younger age of onset.1 In this review, we examine the risks and benefits of routine breast cancer screening starting at age 40 at the individual level, followed by an evaluation of the role of advanced imaging techniques in screening women on a population level.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".