Maxillary Sinus Lift: A Bibliometric and Altmetric Analysis of the 100 Most Cited Articles
Bibliographic record
Abstract
AIM: To analyze the scientific profile of the 100 most cited articles on maxillary sinus lift. MATERIALS AND METHODS: A search was conducted in the Web of Science Core Collection database in February 2024. Two reviewers retrieved the 100 most cited articles. The number of citations for the articles was compared in the Scopus and Google Scholar databases. The VOSviewer software was employed to generate collaborative network maps for authors and keywords. Dimension was consulted to measure altimetry data. Google Trends was used to explore the global popularity of research on maxillary sinus lift. RESULTS: The number of citations ranged from 120 to 1259. The articles were published between the years 1991 and 2017. The most frequent study design was observational (21%), and the surgical technique was the lateral window (76%). The most used bone graft was autogenous (15%). The journal with the highest number of articles was Clinical Oral Implants Research (29%). The United States was the most prevalent country (27%), and New York University had the highest number of publications (8%). However, the European continent stood out (66%). Froum SJ was the author with the highest number of publications (6%). The most common keywords were "dental implants" (38%). Intense mentions were identified primarily on Mendeley. According to Google Trends, Ukraine was the country that researched maxillary sinus lift the most. CONCLUSION: It can be concluded that the 100 most cited articles on sinus elevation were observational studies that evaluated the lateral window technique for sinus elevation.
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) | 0.003 | 0.004 |
| Bibliometrics | 0.188 | 0.130 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".