A Bibliometric Survey of Publications in Vascular Ultrasound
Bibliographic record
Abstract
Access to a body of published research is important to the development of research and to inform quality patient care. Measures of such activities are determined by bibliometric analysis of publication databases. This project aims to identify the scope of such publications in vascular ultrasound and its implications for the ultrasound professionals. Major databases were surveyed to identify the range of publications in “vascular ultrasound” from 2016 to 2018. The topics, sources, and relevance of the publications were noted along with recognized impact factors and other parameters. A list of target journals was created in radiology, ultrasound, and clinical vascular spheres. These journals were assessed for bibliometric parameters, total number of articles, and articles specific to vascular ultrasound. Web of Science was used over a 3-year period to identify 4136 articles (1421, 1384, and 1326 in each of 2016, 2017, and 2018, respectively). This search returned 414 pages; of these, 2-page analysis revealed 15% were relevant to vascular ultrasound practice. Of the 21 “highly cited” articles, one was related to carotid ultrasound and one was aortic practice guidelines. Of the 31 targeted journals (radiology, 5; ultrasound, 16; vascular surgery, 7; vascular medicine, 3), 3873 articles were published; 123 (3.2%) were relevant to vascular ultrasound. The maximum vascular ultrasound content in any one journal was 8%. The activities were guidelines, 11; cases, 7; and mixed other, 105. The topics were carotid, 30; arterial, 17; aorta, 7; venous, 16; education, 12; and other, 41. The impact factor was 0.36 to 16.8 (median = 2.1). None of the targeted journals nor the major databases have much content in vascular ultrasound. The choice of journal for publication should be determined by potential audience rather than the journal itself. As a tool for an environment scan of trends in vascular ultrasound, no journal serves well.
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.
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 | 0.013 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.133 | 0.215 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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, 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".