Breast Cancer in Megapolises of Kazakhstan: Epidemiological Assessment of Incidence and Mortality
Bibliographic record
Abstract
Background: Breast cancer is the most common malignant disease among the female population of Kazakhstan like in many developed countries of the world (Canada, UK, US, Western Europe), and it accounts for every 5th tumor. We aimed to assess the epidemiological aspects of breast cancer incidence and mortality among Almaty and Astana (Now Nur-Sultan), Kazakhstan residents in 2009-2018. Methods: A retrospective study using modern descriptive and analytical methods of epidemiology was conducted to evaluate the breast cancer incidence and mortality in megapolises of Kazakhstan. Results: The average annual age-standardized incidence rate of breast cancer amounted to 61.90/0000 (95% CI=56.2-67.6) in Almaty and 61.20/0000 (95% CI=56.765.7) in Astana. The average age-standardized mortality was 19.20/0000 (95% CI=17.3-21.1) in Almaty and 19.30/0000 (95% CI=17.1-21.4) in Astana. The standardized incidence in the megapolises tended to increase (Тgr=+0.8% in Almaty and Тgr=+1.4% in Astana), while the mortality was decreasing (Тdec=−4.2% in Almaty and Тdec=−1.1% in Astana). According to the component analysis, the growth in the number of breast cancer cases was due to a population increase (ΔP=+130.4% in Almaty and ΔP=+93.2% in Astana), with a notable decrease of factors related to the risk of getting sick (ΔR=−27.9% in Almaty, ΔR=−6.1% in Astana). Conclusion: This is the first epidemiological study to assess the changes in incidence and mortality from breast cancer in megapolises of Kazakhstan because of screening. The results of this study can be used to improve the government program to combat breast cancer.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".