A scientometric analysis of the 100 most cited articles on magnetic resonance guided focused ultrasound
Bibliographic record
Abstract
Background Diagnostic ultrasound has long been a part of a physician’s armamentarium, but transcranial focused ultrasound (FUS) is an emerging treatment of neurological disorders. Consequently, the literature in this field is increasing at a rapid pace.ObjectiveThis analysis was aimed to identify the top-cited articles on FUS to discern their origin, spread, current trends highlighting future impact of this novel neurosurgical intervention.MethodsWe searched the Web of Science database on 28th May 2021 and identified the top 100 cited articles. These articles were analyzed with various scientometric parameters like the authors, corresponding authors, country of corresponding author, journal of publication, year of publication. Citation based parameters including total citations, mean citations per article and mean citations, citation count, and the citation per year, citations per year and co-authors per document were studied as well in addition to Hirsch h-index, g-index, m-index, Bradford’s Law, Lotka’s law and Collaboration index.ResultsThe 100 top-cited articles were published between 1998 and 2019 in 45 different journals. The average citations per document and citations per document per year were 97.78 and 12.47, respectively. The most prolific authors were Hynynen K (Medical Biophysics—Toronto), Elias WJ (Neurosurgery—Virginia), Zadicario (InSightec). The Journal of Neurosurgery published the most top-cited articles (n = 11), and most articles originated from the United States, followed by Canada. Among individual institutions, the University of Toronto was the most productive. Conclusion FUS is an emerging treatment of neurological disorders. With its increasing application, the FUS literature is increasing rapidly. Eleven countries contributed to the top 100 cited articles, with the top 2 countries (the United States and Canada) contributing to more than half of these articles.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.153 | 0.123 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".