Supplementary Material for: A bibliometric analysis of the top 100 most-cited papers concerning dental fluorosis
Bibliographic record
Abstract
A high number of citations can indicate the potential of any specific paper to influence other research and generate changes in clinical practice. Analyzing the most-cited papers in a certain scientific field may assist researchers to identify the influential papers as well their main characteristics. The present study aimed to analyze the 100 most-cited papers concerning dental fluorosis (DF) through a bibliometric review. A search was performed in the Web of Science Core Collection database (WoS-CC) in November 2021. The papers were displayed in descending order according to the number of citations in WoS-CC. Two independent researchers performed the selection. Scopus and Google Scholar were used to compare the number of citations with WoS-CC. The following data were extracted from the papers: title, authors, number and density of citations, institution, country, continent, year of publication, journal title, keywords, study design, and theme. Collaborative networks were generated using the VOSviewer software. The top 100 most-cited papers were published between 1974 and 2014 and were cited 6,717 times (ranging from 35 to 417). Community Dentistry and Oral Epidemiology (24%), Journal of Dental Research (21%), Journal of Public Health Dentistry (17%), and Caries Research (13%) published the most papers. Observational studies (60%) and literature reviews (19%) were the most common study designs. The main topics were epidemiology (44%) and fluoride intake (32%). The countries with the highest number of papers were the United States of America (USA) (44%), Canada (10%), and Brazil (9%). The University of Iowa (USA) had the most papers (12%). Levy SM was the author with the highest number of papers (12%). The 100 most-cited papers on DF were mainly observational studies focused on epidemiology and originated in North America. There were few interventional studies and systematic reviews among the most-cited papers concerning this topic.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.062 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.571 | 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 teacher head, 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".