Botulinum Toxin in Aesthetic Medicine: A Bibliometric Analysis of Research Trends and Methodological Quality of the Top 100 Cited Publications
Bibliographic record
Abstract
Abstract Background Botulinum toxin is widely used in aesthetic medicine, with numerous studies examining its efficacy and safety. Objectives This bibliometric analysis aims to describe research trends and assess the methodological quality of the highest-impact botulinum toxin research in aesthetic applications. Methods The authors of this study identified the 100 most-cited publications on botulinum toxin in aesthetics using Web of Science, covering all available journal years (from inception to October 2024). The Oxford Centre for Evidence-Based Medicine Level of Evidence (LOE) was used to assess the methodological quality of each study. Results The authors identified 1728 articles on the aesthetic uses of botulinum toxin, with the top 100 most-cited articles spanning from 1994 to 2021. The United States dominated the research landscape with 50 articles, followed by Canada (15). The University of California (United States) and the University of British Columbia (Canada) emerged as the top contributing institutions. Among journals, Dermatologic Surgery led in publication count, followed by Plastic and Reconstructive Surgery and Aesthetics Surgery Journal. Notably, Professors Jean Carruthers and Alastair Carruthers from Canada were the leading researchers, topping both publication count and citation metrics. Notably, more than half of the studies were classified as LOE 5 (Expert Opinion/Narrative Review). Conclusions This bibliometric analysis reveals a paucity of high-quality studies in the field of botulinum toxin in aesthetic medicine, with research predominantly concentrated in western countries. These findings highlight the need for more rigorous, evidence-based studies and increased global collaboration to advance the understanding and application of botulinum toxin in aesthetics. Level of Evidence: 4 (Therapeutic)
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 | 0.040 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.219 | 0.218 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".