Landmarks in facial reanimation – a bibliometric analysis of the 50 most cited papers in dynamic facial reconstruction
Bibliographic record
Abstract
BACKGROUND: Advances in the field of facial reanimation surgery have resulted in an increase in the quantity of published research in the international literature. The aim of this work is to provide the reader a synthesized view of the most influential themes, articles and authors in this field. MATERIAL AND METHODS: We searched the Clarivate Analytics Web of Science Citation Index to identify the 50 most cited papers in dynamic facial reanimation in the past 70 years. Data regarding article title, authors, year of publication, total citations and citation index was obtained. Results are presented using descriptive statistics. RESULTS: The most cited articles were distributed in 16 journals. Plastic and Reconstructive Surgery had the highest number of highly cited works with 27 articles, followed by JPRAS (5 papers) and the Journal of Neurosurgery (4 papers). The United States contributed most (17 papers), followed by Canada and Japan (6 each). Dr. Julia K. Terzis was the most cited author (7 works). Case series and comparative studies were the most prevalent type of article published (96%) from 1953 to 2015. The most cited paper focused on free functional muscle transfer (FFMT). Most articles were level IV research, with a mean citation index of 5.27 ± 2.85. CONCLUSION: This collection offers a clear overview of the key milestones and advancements in the field. We expect it serves as a practical resource for clinicians and researchers striving to advance the science and practice of facial reanimation surgery.
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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.192 | 0.165 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".