Amaç: Bu çalışmanın amacı, Yükseköğretim Kurulu (YÖK) Başkanlığı Ulusal Tez Merkezi’nde Ortodonti Ana Bilim Dalı’nda 1968 ile 2023 yılları arasında yayımlanmış olan tezlerin bibliyometrik analizini yapmak ve ilgili tezlerin mevcut durumunu ve gelişimini değerlendirmektir. Gereç ve Yöntem: YÖK Ulusal Tez Merkezi sitesinde Detaylı Tarama sekmesi kullanılarak Ortodonti Ana Bilim Dalı seçilerek yapılan taramada 1745 adet yayımlanmış teze ulaşılmıştır. Elde edilen veriler Microsoft Excel programına aktarılarak listelenmiştir. Microsoft Excel programında tezlerin türlerine, kullanılan dizinlere, yayımlandığı üniversitelere, çalışma alanlarına, yıllara ve sayfa sayılarına göre dağılımı listelenmiştir. Verilerin analizinde temel istatistiksel yöntemler kullanılmıştır. Bulgular: Çalışma bulgularına göre diş hekimliğinde uzmanlık tezi türünde 611, doktora tezi türünde 1053 ve yüksek lisans tezi türünde ise 81 tez yayımlanmıştır. Ortodonti alanındaki lisansüstü tezlerde toplamda 6351 dizin kullanıldığı görülmüştür. En fazla tez yayımlayan üniversite Marmara Üniversitesidir. Tezler çalışma alanlarına göre kategorize edildiğinde en fazla klinik çalışmalar ile ilgili tezler olduğu tespit edilmiştir. 2018 yılı en fazla tezin yayımlandığı yıldır. Tezlerin ortalama olarak 117,07 sayfa uzunluğunda olduğu belirlenmiştir. Sonuç: Diş hekimliğinde uzmanlık sınavının yapılmaya başlanmasıyla lisansüstü tez sayılarında ciddi bir artışın yaşandığı ve doktora tezi türünde yayımlanan tezlerin yıllara göre oransal olarak azaldığı sonucuna varılmıştır. Tez sayısında özellikle 2014 yılından sonra ciddi bir artış gözlenmiştir. Yapılan lisansüstü tezlerde klinik çalışmalar ağırlıklı olmasına rağmen son yıllarda bilgisayar destekli deneysel çalışmalara ilgi giderek artmaktadır.

Objective: The objective of this study was to systematically analyze the cleft lip and/or palate (CL/P) literature published between 2000 and 2025 to identify dominant research themes, temporal trends, and potential gaps by applying a natural language processing-based topic modeling approach. Design: We analyzed articles retrieved from the Scopus database and identified thematic clusters using the BERTopic algorithm, which integrates BERT embeddings, Uniform Manifold Approximation and Projection dimensionality reduction, and Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering. Setting: This study exclusively examined articles related to CL/P. Patients/Participants: This study involved no patients or participants. Interventions: Not applicable as this is a BERTopic-based analysis. Main Outcome Measures: Outcomes included identifying dominant research topics, trend analysis, distribution across countries and journals, and classifying topics as “hot” or “cold” based on temporal trends. Results: We identified 29 thematic clusters. The most common topics included “Velopharyngeal Dysfunction and Fistula Repair,” “Nonsyndromic Cleft Lip and Palate and Genetic Associations,” and “Unilateral Cleft Lip and Palate and Maxillofacial Asymmetry.” “Quality of Life and Psychosocial Impact in Children,” “Surgical Repair Approaches,” and “Presurgical Nasoalveolar Molding Techniques” emerged as “hot topics.” The United States led in publication volume, while certain topics dominated in specific countries. The Cleft Palate-Craniofacial Journal published the most articles. Conclusions: This study provides a comprehensive thematic map of CL/P research, highlighting strengths and underexplored areas. BERTopic offers an efficient, scalable method for analyzing large-scale scientific literature and identifying future research priorities.

The aim of this study was to evaluate the effects of interradicular and infrazygomatic miniscrew anchorages on tooth movement and von Mises stress distribution during two-step and en-masse retraction using three-dimensional finite element analysis. Eight finite element models were constructed combining two retraction mechanics (en-masse and two-step), two anchorage types (infrazygomatic and interradicular miniscrews), and two power arm heights (3 and 6 mm). A 150 g retraction force was applied along the line connecting the miniscrew and power arm. Static structural analyses were performed in ANSYS to calculate tooth displacement (x-, y-, and z-axes) and von Mises stress distribution. Stress was mainly concentrated in the cortical bone around miniscrew insertion areas, as well as on the lateral incisors and canines. Increasing the power arm height from 3 mm to 6 mm reduced uncontrolled tipping and extrusion, improving biomechanical control. Infrazygomatic anchorage produced less tipping and extrusion than interradicular anchorage in both mechanics. Two-step retraction generally showed slightly more controlled tooth movement compared with en-masse retraction. This finite element simulation showed that the two-step retraction approach with infrazygomatic anchorage and a 6-mm power arm resulted in comparatively reduced tipping and a more distributed stress pattern under the conditions of the present model. However, complete translational movement was not achieved in any model. As finite element models cannot fully replicate clinical conditions, these findings should be interpreted with caution and validated through clinical studies.

The aim of this study was to compare the response capabilities of generative pre-trained transformers (GPTs) for identical lateral cephalograms (LCs) based on text- and image-based prompts, using the newly developed Artificial Intelligence Based Diagnosis and Treatment Planning Index (AIDTI). A total of 90 LCs from 30 cases each with skeletal Class I, II, and III malocclusions were included. The LCs were presented to GPT-4o, GPT-o3 pro, GPT-5, and GPT-5 pro in two different formats: text-based (numerical data including cephalometric analysis measurements) and image-based (direct image upload) prompts. The responses of the GPTs were evaluated using the newly developed AIDTI, scored on a 0-10 scale and consisting of five criteria: diagnostic accuracy, capability for differential diagnosis, clinical appropriateness of the proposed treatment plan, disclosure of risks and complications associated with the treatment plan, and ability to offer alternative treatment options. Higher performance was observed in text-based prompts, with the highest score achieved by GPT-5 pro (9.62 ± 1.13). By contrast, the performance of all GPTs on image-based prompts was notably lower, with the highest score being 4.16 ± 4.12 for GPT-o3 pro. Additionally, all models showed a tendency to randomly categorize malocclusions as Class II, suggesting systematic bias in their predictions. AIDTI provides a structured and multidimensional framework for the concise, balanced, and clinically meaningful interpretation of GPT performance. Because GPTs are unable to directly analyze LCs, their use as independent or reliable supportive tools in orthodontics remains limited at this stage. GPTs may contribute more reliably to orthodontic practice only when orthodontists prompt the model with text-based cephalometric measurement data. Not applicable.

The aim of this study was to evaluate the performance of artificial intelligence (AI)-based large language models (LLMs) in predicting cervical vertebral m