Basic Cancer Research in the UAE
Bibliographic record
Abstract
Abstract Like many other fields in the UAE, cancer research showed a significant achievement. Measuring research outcomes is considered a crucial and critical step in evaluating the research impact. In this chapter, we analyzed various indicators that measure the number and impact of cancer research performed within the UAE in the last decades using the PubMed search engine and the Scopus database. Our results showed an exponential increment in cancer-related publications, from only 66 in 2011 to 865 in 2021. The increment in the number of publications was also associated with an improvement in the quality of cancer-related manuscripts, and this is presented as publication in more diverse and high-ranking journals, including PLOS One, Scientific Reports, Molecules, Asian Pacific Journal of Cancer Prevention, Annals of the New York Academy of Sciences, and Lancet. While United Arab Emirates University, the University of Sharjah, and Tawam Hospital were shown to be the top UAE-affiliated institutions, the National Institutes of Health, the National Cancer Institute, the Medical Research Council (MRC), and the US Department of Health and Human Services represent the top international funding bodies and collaborators. In conclusion, our analysis revealed an exponential increase in cancer research productivity, which is also coupled with improvements in the quality and impact of those research activities. This was achieved through investment in research infrastructure, recruitment of experienced researchers, and the establishment of various training programs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".