Similarities and disparities in cancer burden among Arab world females.
Bibliographic record
Abstract
INTRODUCTION: Cancer is the leading cause of increased morbidity and mortality worldwide. This work aims to study the Arab-world females' cancers (AFCs), the similarities and disparities from epidemiological, economic and development-indices points of view. MATERIALS AND METHODS: Descriptive - Analytical review of the 2018 Global Cancer Observatory concerning AFCs. Data on various cancers were compiled and compared among the countries in the regions and the world females' cancers (WFCs). RESULTS: A total estimate of 227,494 new AFCs; 2.64% of WFCs, with an average crude incidence rate of 111.7* and an age-standardized rate of 134.5*, compared to 228* and 182.6* of WFCs, respectively. Death cases estimated to be 122,903; 2.95% of WFCs, with an average crude mortality rate of 60.3* and age-standardizedrate of 75.4*, compared to 110.2* and 83.1* of WFCs, respectively. Five-year prevalent cases were 530,735; 2.33% of WFCs, with an average proportion of 260.5*, compared to 603.5* of WFCs. Mortality to Incidence Ratio was 0.54 (range 0.36 - 0.80), compared to 0.58, 0.52, 0.49 in the medium human development index, upper-middle-income countries and world countries, respectively. */100,000 population. CONCLUSIONS: Despite the demographic and cultural similarities among the Arab communities, there are apparent disparities in AFCs. A systematic approach is required to address these remarkable differences in cancer ranking and rates among Arab countries themselves and when compared to other world groups and nations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".