Cancer prevalence, incidence, and mortality rates in Afghanistan in 2020: A review study
Bibliographic record
Abstract
BACKGROUND: Afghanistan is in an epidemiological transition, as cancer is the second leading cause of mortality due to non-communicable diseases. This study is the first to provide a comprehensive perspective on the overall cancer situation in Afghanistan by discussing the top five most common cancers, their incidence variations, risk factors, and preventive measures. The limited number of cancer studies conducted in Afghanistan highlights the importance of the present review. RECENT FINDINGS: This article provides an overview of cancer burden in Afghanistan in 2020. It utilizes IARC-generated GLOBOCAN 2020 data for one, three, and five-year prevalence rates, the estimated number of new cancer cases, and mortality rates by age group in Afghanistan. According to GLOBOCAN, the top five common cancers in both sexes in Afghanistan were breast (n = 3173, 14.3%), stomach (n = 2913, 7.8%), lung (n = 1470, 6.6%), cervix uteri (n = 1200, 5.4%), and colorectum (n = 1084, 4.9%). CONCLUSION: This study provides a brief overview of the general cancer situation in Afghanistan, and a more in-depth analysis of the five common cancers identified. Effective therapies, awareness, and prevention initiatives targeting lifestyle, immunization, early diagnosis, and environmental risk factors are essential for addressing the impact of population growth and aging on cancer incidence in Afghanistan. Further research and extensive studies are needed to better understand cancer burden in the country.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".