Cancer Burden Among Arab-World Females in 2020: Working Toward Improving Outcomes
Bibliographic record
Abstract
PURPOSE: Cancer is the leading cause of morbidity and mortality worldwide. This work presents the Arab-world females' cancers (AFCs) statistics in 2020, compared with the 2018 AFCs statistics, the Arab-world male cancers statistics, and the world females' cancers (WFCs) statistics in 2020. This can help set the stage for a better policy for cancer control programs and improve outcomes. MATERIALS AND METHODS: A descriptive review of the 2020 Global Cancer Observatory concerning AFCs was performed. Data on various cancers were compiled and compared among the countries in the region and WFCs. RESULTS: A total estimate of 244,317 new cases and 132,249 deaths is reported in AFCs; representing 2.65% and 2.99% of WFCs, respectively, with an average crude incidence/mortality ratio of 116.2 (/100,000 population)/62.9 (/100,000 population) and an age-standardized incidence/mortality ratio of 137.7(/100,000 population)/77.2(/100,000 population) compared with 238.8(/100,000 population)/114.6(/100,000 population) and 186(/100,000 population)/84.2(/100,000 population) of WFCs, respectively. Five-year prevalent cases were 585,295; 2.28% of WFCs. In comparison to males, females accounted for 47.8% of the whole population, 52.9% in incidence, 46.9% in mortality, and 56.9% in the prevalence of patients with cancer. Mortality-to-incidence ratio (MIR) was 0.54 (range 0.39-0.62 in Arab countries, compared with 0.48 globally), and it ranged from 0.14 to 0.97 in the 30 AFC types. Breast cancer was the most common cancer in incidence and mortality, with an MIR of 0.39. CONCLUSION: The 2020 descriptive analysis of the females' cancers in the Arab world revealed a relatively high MIR compared with females' cancers worldwide; a lower MIR compared with the males; and comparable MIR to 2018 one. We call for more in-depth studies to determine the causes of these differences that might translate into actionable interventions and better outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".