Research hotspots and trends analysis of ankylosing spondylitis: a bibliometric and scientometric analysis from 2009 to 2018
Bibliographic record
Abstract
BACKGROUND: This study utilized bibliometric analysis to qualitatively and quantitatively analyze hotspots and predict trends in the field of ankylosing spondylitis (AS) research. METHODS: Articles about AS were obtained from the Web of Science Core Collection and PubMed database, and bibliometric analysis was carried out through CiteSpace and the Online Analysis Platform of Literature Metrology and Bibliographic Item Co-Occurrence Matrix Builder (BICOMB). Then, co-word biclustering analysis was conducted to obtain research hotspots and predict trends using gCLUTO software. RESULTS: was the leading journal in AS research, with an impact factor (IF) of 3.634 and H-index value of 49. In terms of region, the United States led the world in this field, and The University of Toronto was the leading institution for AS research. Van Der Heijde, D was the most prolific author in the field. Eight research hotspots in the field of AS were also identified. CONCLUSIONS: Our analysis identified eight research hotspots, and predicted that surgical treatment and etiology will be the main AS research trends in the future. This study provides new directions and ideas for future research in AS.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.117 | 0.146 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".