Benefits and harms of breast cancer screening with mammography in women aged 40–49 years: A systematic review
Bibliographic record
Abstract
Early detection of breast cancer through screening can lower breast cancer mortality rates and reduce the burden of this disease in the population. In most western countries, mammography screening starting from age 50 is recommended. However, there is debate about whether breast cancer screening should be extended to younger women. This systematic review provides an overview of the evidence from RCTs on the benefits and harms of breast cancer screening with mammography in women aged 40-49 years. The quality of the evidence for each outcome was appraised using the GRADE approach. Four articles reporting on two different trials-the Age trial and the Canadian National Breast Screening Study-I (CNBSS-I)-were included. The results showed no significant effect on breast cancer mortality (Age trial: RR 0.93 (95% CI 0.80-1.09); CNBSS-I: HR 1.10 (95% CI 0.86-1.40)) nor on all-cause mortality (RR 0.98, 95% CI 0.93-1.03) in women aged 40-49 years offered screening. Among regularly attending women, the cumulative risk of experiencing a false-positive recall was 20.5%. Over-diagnosis of invasive breast cancer at 5 years post-cessation of screening for women aged 40-49 years was estimated to be 32% and at 20 years post-cessation of screening to be 48%. Including ductal carcinoma in situ, these numbers were 41% and 55%. Based on the current evidence from randomised trials, extending mammography screening to younger age groups cannot be recommended. However, there were limitations including relatively low sensitivity of screening and screening attendance, insufficient power, and contamination, which may explain the nonsignificant results.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".