Top 100 most‐cited oral health‐related quality of life papers: Bibliometric analysis
Bibliographic record
Abstract
OBJECTIVE: This study assessed the features of the 100 most-cited papers on oral health-related quality of life (OHRQoL). METHODS: The 100 most-cited OHRQoL papers were collected from Web of Science, adopting a combined keyword search strategy. Google Scholar and Scopus databases were searched to compare citations. The following data were extracted from papers: title of the paper, number of citations, authorship, country, year of publication, title of the journal, study design, sample size, topic and OHRQoL instruments used. Graphical bibliometric networks were created using VOSviewer software. RESULTS: The number of citations of the top 100 most-cited OHRQoL papers ranged from 73 to 949. Fifty-six papers received at least 100 citations and two received more than 400 citations. Most papers were from Canada (23%) and had been published in Community Dentistry and Oral Epidemiology (37%). David Locker was the most-cited author (25 papers; 3,521 citations). The cross-sectional study design was the most common (68%). The impact of oral health conditions on OHRQoL (43%) was the most frequent topic, and the Oral Health Impact Profile (OHIP) was the most commonly used OHRQoL instrument (48%). CONCLUSIONS: This bibliometric analysis highlighted the characteristics of the 100 most-cited OHRQoL papers, demonstrating that this field is far from saturated. This list of the most-cited articles can provide a reference point to guide oral health research, education and services.
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 | 0.020 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.175 | 0.177 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".