<scp>Peri‐implantitis</scp>: A bibliometric network analysis of top 100 <scp>most‐cited</scp> research articles
Bibliographic record
Abstract
BACKGROUND: Over the past decades, there has been an increase in research publications focusing on peri-implantitis. When facing limited healthcare resources, bibliometric analyses can guide researchers and funding parties toward areas where reallocation or more focus on research activity is warranted. The main objective of this study was to identify the trends of the top 100 cited articles on peri-implantitis research as the first study of its kind. METHODS: A Web of Science search, using the keywords "peri-implantitis or periimplantitis" was built to create a database of the most-cited articles. Articles were ranked by citation count and screened by two independent reviewers. The bibliometric characteristics of the studies were gathered and analyzed using several bibliometric software. Author collaborations, author clusters, and keyword co-occurrence network analyses were also performed. The correlation between the citation count and the age of each article was tested. RESULTS: The top 100 cited papers were published from 1994 to 2018 and the total citation counts ranged from 119 to 972 with 244.5 citations/paper on average. There was no correlation between the age of the articles and the citation count (p-value = 0.67). 21% of the studies consisted of prospective clinical studies. 35% of the papers focused on treatment and prevention of peri-implantitis while 65% concerned epidemiology. The top three most prolific countries were Sweden (n = 31), Germany (n = 15), and Switzerland (n = 13). We found 12 authors who had greater than five publications on the list. Also, the most published journal was Clinical Oral Implants Research. CONCLUSION: This study provides insight into the characteristics and quality of the most highly cited peri-implantitis literature. This revealed a deficiency in terms of the number of studies on treatment strategies as well as a higher level of evidence studies among the most- impactful papers on peri-implantitis at the moment.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.137 | 0.122 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".