Bibliometric analysis of the GentleWave system: trends, collaborations, and research gaps
Bibliographic record
Abstract
Objectives: The study aimed to conduct a bibliometric analysis of the GentleWave system (Sonendo, Inc.). Methods: An electronic search was conducted in June 2024 using the Web of Science Collection database. Two reviewers independently screened publications, extracting data on authorship, publication details, study design, and citation metrics. Statistical analyses were performed in R to assess variable correlations, while the VOSviewer (Visualization of Similarities Viewer) software was used to map author and keyword networks. Results: The search yielded 47 records, with 32 studies included. Publications spanned 2014 to 2024. The Journal of Endodontics published the highest number of studies (n = 15), and the International Endodontic Journal had the highest impact factor (5.4). The University of British Columbia and Sonendo, Inc. were the most frequent affiliations. Among the 32 articles, 28 were in vitro studies, primarily focusing on microbiology (n = 9). A total of 95 authors were identified, with Haapasalo and Shen being the most cited (n = 229). The articles accumulated 495 citations, demonstrating a strong positive correlation between the number of studies and citation counts (r = 0.98). Conclusions: The analysis highlights a predominance of in vitro studies. Geographic concentration in the United States and Canada limits diversity, while the strong correlation between study numbers and citations suggests that increased publication volume enhances visibility.
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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 | 0.034 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.188 | 0.208 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".