Application of Machine Learning in Orthodontics: A Bibliometric Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Machine learning (ML), a facet of artificial intelligence, utilizes algorithms to learn from data without explicit programming. In orthodontics, ML offers advantages like tailoring personalized treatment plans for patients. Despite its potential, there hasn’t been a bibliometric analysis of ML studies in orthodontics. This study aims to fill that gap. Types of studies reviewed: Articles on ML in orthodontics were reviewed from Web of Science Core Collection, Embase, Scopus, and PubMed. Data on journal details, country of origin, publication month, citations, keywords, and co-authorship were extracted. Results: The search retrieved a total of 1478 articles, of which 701 were excluded. American Journal of Orthodontics and Dentofacial Orthopedics has published the most articles (3.6%), followed by the seminars in Orthodontics Journal (1.6%), and Orthodontics and Craniofacial Research Journal (1.6%). Most of the articles were from researchers from China (n = 156), the United States (n = 107), and South Korea (n = 70). The number of citations of the published articles ranged from 0 to 702, with most articles (75.54%) having at least one citation. Science Mapping analysis revealed that the most used keywords were Human(s) (n = 484), Artificial intelligence (n = 194), Female (n=169), Male (n = 161), and Cephalometry (n = 151). Clinical implications: Clinicians should be aware of the emerging global collaborative landscape in machine learning trends, stay informed about technological advancements, and consider the potential impact of ML on patient care and treatment outcomes in their practices.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.029 | 0.072 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it