The publication trends and hot spots of scoliosis research from 2009 to 2018: a 10-year bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: This study aims to quantitatively and qualitatively investigate the trends in scoliosis research and evaluate research hotspots using bibliometric analysis. METHODS: All relevant publications on scoliosis from the period from 2009 to 2018 were extracted from the Web of Science and PubMed databases. Publication trends were analyzed using an Online analysis platform of literature metrology, Bibliographic Item Co-occurrence Matrix Builder (BICOMB), and CiteSpace software. Hotspots were analyzed and visualized using the gCLUTO software package. RESULTS: A total of 7,445 scoliosis research publications dated between 2009 and 2018 were found. The spine was the most popular journal in this field during this period. The United States maintained a top position in global scoliosis research throughout the 10 years and has had a pivotal influence, followed by China and Canada. Among all institutions, the University of California, San Francisco, was a leader in research collaboration. At the same time, Professors Yong Qiu and Lawrence G. Lenke made great achievements in scoliosis research. We analyzed the major Medical Subject Headings (MeSH) terms/MeSH subheadings and identified eight hotspots in scoliosis research. CONCLUSIONS: We summarized the publication information of scoliosis-related literature in the 10 years from 2009 to 2018, including country and institution of origin, authors, and publication journal. We analyzed former research hotspots in the field of scoliosis and predicted future areas of interest. The development of various new orthopedic plants, artificial intelligence diagnosis, and genetic research will be future hotspots in scoliosis research.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.120 | 0.138 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".