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 (WoS-CC) database 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 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.
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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 | 0.022 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.233 | 0.211 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".