Mortality of Young Women due to Breast Cancer in Low, Middle and High-Income Countries: Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To identify the difference in breast cancer mortality rates among young women according to countries' economic classification. METHODS: A systematic literature review included retrospective studies on breast cancer mortality rates in women aged 20 to 49 years. Databases used were PubMed, Web of Science, Scopus, and Virtual Health Library, with articles selected in English, Portuguese, and Spanish. The study selection and analysis were conducted by two pairs of researchers. Data from 54 countries were extracted, including 39 high-income, 12 upper-middle-income, and 3 lower-middle-income countries. A meta-analysis was performed with the quantitative data from two studies. RESULTS: Six articles met the inclusion criteria. Four were analyzed descriptively due to data diversity, and two were included in the meta-analysis. The pooled mortality rate for high-income countries was 10.2 per 100,000 women (95% CI: 9.8-10.6), while for upper-middle-income countries, it was 15.5 per 100,000 women (95% CI: 14.9-16.1). Lower-middle-income countries had a pooled mortality rate of 20.3 per 100,000 women (95% CI: 19.5-21.1). The decrease in mortality rates in high-income countries was statistically significant (p<0.05). CONCLUSION: Mortality rates for breast cancer among young women have decreased significantly in high-income countries but have increased in lower-income countries. This disparity underscores the impact of insufficient investment in preventive measures, health promotion, early diagnosis, and treatment on young women's mortality in lower-income countries.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".