Cancer research in the United Arab Emirates from birth to present: A bibliometric analysis
Bibliographic record
Abstract
Background: Accumulating evidence indicates that the incidence of cancer is increasing in the United Arab Emirates (UAE). This analysis aimed to determine the current cancer research output in the UAE to guide future national research. Methods: The Scopus database was searched for cancer-related bibliographic data from the UAE. The number of publications, citation analysis, co-authorship of the author, institution, and country, keyword co-occurrence, and reference co-citations were analyzed using the R-studio bibliometrics package and VOSviewer software. Results: A total of 1678 journal articles were retrieved from 1981 to 2022. Cancer research in the UAE (UCR) is increasing at a rate of 14.64% (R-squared = 0.75; F = 46.477; P<0.001). The UAE had a 0.06% participation rate in terms of the number of original articles. The rate of international co-authorship is 40.23%. The U.S.A., U.K., Egypt, Saudi Arabia, India, and Canada had more than 100 co-authored documents from 156 countries that collaborated with the U.A.E. Conclusions: Compared to other nations, the UAE has fewer publications on cancer, although the number is growing. The current report provides an up-to-date and in-depth summary of the trends in UCR. This project is an excellent place for researchers interested in conducting data-mapping work in this field.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.121 | 0.159 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".