Cleft Lip and Palate Research Trends in Saudi Arabia: A Bibliometric Analysis
Bibliographic record
Abstract
As Saudi Arabia advances in medical education and patient care, assessing its contribution to cleft lip and palate (CLP) research is vital. This bibliometric analysis aims to map the trends, collaborations, and impact of Saudi research in CLP. Utilizing the Web of Science database, this study conducted a comprehensive bibliometric analysis of CLP research related to Saudi Arabian publications. The analysis included data extraction and assessment of publications, citations, H-index, and international collaborations using advanced bibliometric tools and software. A total of 89 CLP-related articles in Saudi Arabia were retrieved. The findings indicated a steady increase in publications and citations over the years, reflecting growing interest and recognition of CLP's challenges in the Saudi healthcare context. King Abdulaziz University and King Saud University emerged as leading contributors. International collaboration was evident, with significant partnerships with countries like the USA, Canada, the UK, and others. The Cleft Palate-Craniofacial Journal and the Saudi Dental Journal were identified as the most influential journals in disseminating Saudi CLP research. The study highlights a positive growth trajectory in Saudi CLP research, marked by increased publications, citations, and international collaborations. It underscores the importance of continuous research and the need for enhanced efforts to further the understanding and treatment of CLP. Future studies should consider including a broader range of databases to provide a more comprehensive global view of CLP research trends.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.096 | 0.107 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".