Cancer Burden Among Arab World Males in 2020: The Need for a Better Approach to Improve Outcome
Bibliographic record
Abstract
PURPOSE: Cancer is a leading cause of increased morbidity and mortality worldwide. This work aims to study the Arab world males' cancers (AMCs) and the similarities and disparities with the world males' cancers (WMCs) from different burden points of view. MATERIALS AND METHODS: A descriptive review of the 2020 Global Cancer Observatory revealed AMCs compared with the 2020 WMCs and the 2018 AMCs. Data on the top 27 AMCs were compared among the region's countries and the world groups. RESULTS: In 2020, a total estimate of 217,203 new AMCs, 2.2% of WMCs, with an average age-standardized rate of 133.5/100,000 population, compared with 222/100,000 population of WMCs, was observed. Death estimates were 148,395, 2.7% of WMCs, with an average age-standardized rate of 95/100,000 population, compared with 120.8/100,000 population of WMCs. The five-year prevalence was observed in 442,014, 1.8% of WMCs. The average AMC mortality to incidence ratio (MIR) was 0.68, compared with 0.55 in WMCs and 0.54 in Arab females. Lung cancer was the top in incidence and mortality, whereas penile cancer was the lowest. The range of MIRs among the 27 cancer types was 0.19-0.96. CONCLUSION: The descriptive review of the 2020 males' cancers in the Arab world revealed a relatively high MIR, compared with males' cancers worldwide and the females' cancers in the Arab world. This requires further evaluation to discern the underlying causes and address them systematically. More cancer control actions are warranted.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".