Mapping the Iranian Research Literature in the Field of Traditional Medicine in Scopus Database 2010-2014.
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to provide research and collaboration overview of Iranian research efforts in the field of traditional medicine during 2010-2014. METHODS: This is a bibliometric study using the Scopus database as data source, using search affiliation address relevant to traditional medicine and Iran as the search strategy. Subject and geographical overlay maps were also applied to visualize the network activities of the Iranian authors. Highly cited articles (citations >10) were further explored to highlight the impact of research domains more specifically. RESULTS: About 3,683 articles were published by Iranian authors in Scopus database. The compound annual growth rate of Iranian publications was 0.14% during 2010-2014. Tehran University of Medical Sciences (932 articles), Shiraz University of Medical Sciences (404 articles) and Tabriz Islamic Medical University (391 articles), were the leading institutions in the field of traditional medicine. Medicinal plants (72%), digestive system's disease (21%), basics of traditional medicine (13%), mental disorders (8%) were the major research topics. United States (7%), Netherlands (3%), and Canada (2.6%) were the most important collaborators of Iranian authors. CONCLUSION: Iranian research efforts in the field of traditional medicine have been increased slightly over the last years. Yet, joint multi-disciplinary collaborations are needed to cover inadequately described areas of traditional medicine in the country.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.126 | 0.140 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".