Top 100 most‐cited papers in core dental public health journals: bibliometric analysis
Bibliographic record
Abstract
OBJECTIVE: This quali-quantitative study analysed the 100 most-cited papers in core dental public health (DPH) journals focusing on understanding international knowledge production. METHODS: The DPH journals were selected from titles and scopes at Web of Science Core Collection database up to March 2020. Further comparisons were performed at Scopus and Google Scholar databases. Some bibliometric parameters were extracted as follows: title, number of citations, citation density (number of citations per year), first author's country, year of publication, study design and subject. VOSviewer software was used to create graphical bibliometric maps. RESULTS: Papers were ranked by the total number of citations, which ranged from 104 to 1,019, and six papers were cited more than 400 times. Papers were published from 1974 to 2013, mainly in Community Dentistry and Oral Epidemiology. Most frequent study designs were cross-sectional (30%) and nonsystematic review (25%). Most papers were from Europe (54%) and North America (31%). First authors were predominantly from the United Kingdom (17%), United States of America (17%) and Canada (14%). VOSviewer map of co-authorship demonstrated the existence of clusters in the research collaboration. Although epidemiology was the most frequent subject (84%), health services research presented eight times higher citation density. CONCLUSIONS: Top 100 most-cited papers in core DPH journals were predominantly observational studies from Anglo-Saxon countries. Top 100 most-cited papers in core DPH journals tend to be cross-sectional studies carried out in the United States with highest citation in health services research. Locker D, Petersen PE and Sheiham A are a landmark for DPH field.
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 | 0.018 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.157 | 0.145 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".