Effectiveness of Mammography Screening in Reducing Breast Cancer Mortality in Women Aged 39–49 Years: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Mammography screening of women >50 years of age significantly reduces breast cancer mortality in randomized controlled trials (RCTs). We sought to evaluate the effectiveness of mammography screening in women aged 39-49 years in reducing breast cancer mortality and to discuss previously published meta-analyses. METHODS: PubMed/MEDLINE, OVID, COCHRANE, and Educational Resources Information Center (ERIC) databases were searched, and extracted references were reviewed. Dissertation abstracts and clinical trials databases available online were assessed to identify unpublished works. All assessments were independently done by two reviewers. All trials included were RCTs, published in English, included data on women aged 39-49, and reported relative risk (RR)/odds ratio (OR) or frequency data. RESULTS: Nine studies were identified: the Kopparberg, Ostergotland (The Two-County study), Health Insurance Plan (HIP), Canada, Stockholm, Gothenburg, Edinburgh, Age, and Malmo trials. The individual trials were quality assessed, and the data were extracted using predefined forms. Using the DerSimonian and Laird random effects model, the results from the seven RCTs with the highest quality score were combined, and a significant pooled RR estimate of 0.83 (95% confidence interval [CI] 0.72-0.97) was calculated. Post hoc sensitivity analyses excluding studies with randomization before 1980 caused a loss of statistical significance (RR 0.87, 95% CI: 0.56, 1.13). CONCLUSIONS: Mammography screenings are effective and generate a 17% reduction in breast cancer mortality in women 39-49 years of age. The quality of the trials varies, and providers should inform women in this age group about the positive and negative aspects of mammography screenings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".