Prostate cancer screening in the Middle East and North Africa: a cross-sectional study on current practices
Bibliographic record
Abstract
BACKGROUND: Prostate cancer is a substantial health concern in the Middle East and North Africa region, with many cases diagnosed at advanced stages, a high mortality to incidence ratio, and low prostate cancer awareness. This study aimed to evaluate prostate cancer screening practices in the region to inform effective early detection and management strategies. METHODS: A cross-sectional survey was conducted from July 1, 2023, to November 8, 2024, among physicians from 19 countries in the Middle East and North Africa region. The study used a validated questionnaire to assess prostate cancer screening practices, barriers, and educational needs. RESULTS: The survey had a response rate of 96.8% and 1163 participants. Of these participants, 34.7% routinely performed prostate cancer screenings, with 61.1% using prostate-specific antigen tests. The primary barrier was lack of patient awareness (51.2%). In addition, 65.3% of participants had no formal training. To improve screening rates, participants suggested better patient education (63.5%), increased training for health-care professionals (41.9%), and improved access to screening equipment (38.9%). CONCLUSION: This study revealed that prostate cancer screening rates were low, with barriers including a lack of patient awareness and formal training among physicians. Addressing these issues through culturally tailored education programs may improve early detection rates and ultimately reduce the burden of prostate cancer in the Middle East and North Africa region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".