Trends and Gaps in Colorectal Cancer Screening Research in the Arab World: A 16-Year Bibliometric Analysis (2007–2023)
Bibliographic record
Abstract
Colorectal cancer (CRC) is a significant public health concern, ranking third in incidence and second in mortality worldwide. Despite rising CRC incidence rates in the Arab world, understanding of trends and patterns in CRC screening research remains limited. This study addresses this gap through a bibliometric analysis of CRC screening research in the Arab world from 2007 to 2023. We conducted an extensive literature search in Web of Science and Scopus databases, analyzing 124 articles using the Bibliometrix Package in R. Our findings revealed a 16.5% annual growth in research output, with significant increases from 2014 onwards. Saudi Arabia led in scientific production, followed by Lebanon, Jordan, and Egypt, while Qatar had the highest country production when adjusted for population size. Disparities in research output relative to the CRC burden were evident, especially in lower-resource countries. Three regional clusters were identified: Saudi Arabia, with strong collaborations with Canada and Egypt; a second cluster including Lebanon, UAE, Jordan, Qatar, Iraq, and Oman; and a third cluster featuring Morocco, with significant collaboration with France. Thematic analysis showed a focus on CRC screening awareness, barriers, and adherence but a lack of studies on implementation strategies and cost-effectiveness. This analysis highlights significant trends and gaps in CRC screening research in the Arab world, underscoring the need for increased investment in CRC research and screening initiatives to improve outcomes in the 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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.100 | 0.149 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".