Bibliometric analysis of rheumatology research in the Arab countries
Bibliographic record
Abstract
Abstract Background The Arab world has seen an increase in the burden of musculoskeletal diseases. No bibliometric studies have characterized rheumatology research in the Arab world. This study evaluates the productivity and impact of rheumatology research in the Arab world. Methods We searched the Web of Science Core Collection for rheumatology publications, from 1976 to 2014, for each of the Arab League (AL) countries, North America, Europe and Asia. For the AL countries, the overall trend of publications and citations was analyzed, while considering the paper type and collaborations. Results The AL countries published 944 rheumatology papers over the period studied. The number of publications increased by a factor of 2.77 (95 % CI, 2.75–2.78) each decade, and citations increased by a factor of 2.36 (95 % CI, 0.96–5.82). The absolute number of papers included in the top-10 rheumatology journals remained constant but the proportion decreased. Papers involving collaboration among AL countries were found to increase over time. Conclusions Overall, the AL countries lag in research productivity and impact compared to other regions. Three countries are responsible for the majority of publications, while four countries receive the majority of citations.
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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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.122 | 0.172 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".