Research hotspots and trends analysis of ankylosing spondylitis: a bibliometric and scientometric analysis from 2009 to 2018
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.092 | 0.280 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it