Global research trends in spinal ultrasound: a systematic bibliometric analysis
Bibliographic record
Abstract
Background In recent years, there has been increased interest in the use of ultrasound technology in the evaluation of spinal and paraspinal regions. Objective This study aimed to investigate trends in spinal ultrasound research from 1994 to 2015 and compare the contributions of such research from different countries and authors. Study design Bibliometric analysis. Setting Publications related to spinal ultrasound from 1994 to 2015 were retrieved from the Web of Science database. Methods Excel 2013, GraphPad Prism 5, and VOSviewer were used to summarise bibliometric features, including the number of publications, citation frequency, H-index, and country contributions and hotspots (keywords of popular scientific fields). Results A total of 3859 papers were included. The global inflection point (the point in time when the publication growth rate moved from positive to negative) came in 2010. The United States contributed the largest percentage of articles (1041; 26.9%), with the most citations (19 848) and the highest H-index (61). The journals Osteoporosis International and Spine had the highest publication number. The University of Toronto and the University of California, San Francisco were the most contributive institutions. Studies could be divided into three clusters: surgery, osteoporosis, and others. The keywords ‘adolescent idiopathic scoliosis’ and ‘anaesthesia’ were the latest hotspots, appearing around 2012. Conclusion Spinal ultrasound literature has grown continuously over the last 22 years, with the rate slowing down after 2010. The United States was the largest contributor in this field. Recent studies on topics related to ‘adolescent idiopathic scoliosis’ and ‘anaesthesia’ were relatively new and should be closely followed in spinal ultrasound research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.028 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.232 | 0.250 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".