Objectives The aim of this study is to evaluate the accuracy and completeness of the answers given by Chat Generative Pre-trained Transformer (ChatGPT) (OpenAI OpCo, LLC, San Francisco, CA), to the most frequently asked questions on different topics in the field of periodontology. Methods The 10 most frequently asked questions by patients about seven different topics (periodontal diseases, peri-implant diseases, tooth sensitivity, gingival recessions, halitosis, dental implants, and periodontal surgery) in periodontology were created by ChatGPT. To obtain responses, a set of 70 questions was submitted to ChatGPT, with an allocation of 10 questions per subject. The responses that were documented were assessed using two distinct Likert scales by professionals specializing in the subject of periodontology. The accuracy of the responses was rated on a Likert scale ranging from one to six, while the completeness of the responses was rated on a scale ranging from one to three. Results The median accuracy score for all responses was six, while the completeness score was two. The mean scores for accuracy and completeness were 5.50 ± 0.23 and 2.34 ± 0.24, respectively. It was observed that ChatGPT's responses to the most frequently asked questions by patients for information purposes in periodontology were at least "nearly completely correct" in terms of accuracy and "adequate" in terms of completeness. There was a statistically significant difference between subjects in terms of accuracy and completeness (P<0.05). The highest and lowest accuracy scores were peri-implant diseases and gingival recession, respectively, while the highest and lowest completeness scores were gingival recession and dental implants, respectively. Conclusions The utilization of large language models has become increasingly prevalent, extending its applicability to patients within the healthcare domain. While ChatGPT may not offer absolute precision and comprehensive results without expert supervision, it is apparent that those within the field of periodontology can utilize it as an informational resource, albeit acknowledging the potential for inaccuracies.

Objectives: This study assessed the ability of ChatGPT, an artificial intelligence(AI) language model, to determine the stage, grade, and extent of periodontitis based on the 2018 classification. Materials and methods: This study used baseline digital data of 200 untreated periodontitis patients to compare standardized reference diagnoses (RDs) with ChatGPT findings and determine the best criteria for assessing stage and grade. RDs were provided by four experts who examined each case. Standardized texts containing the relevant information for each situation were constructed to query ChatGPT. RDs were compared to ChatGPT's responses. Variables influencing the responses of ChatGPT were evaluated. Results: ChatGPT successfully identified the periodontitis stage, grade, and extent in 59.5%, 50.5%, and 84.0% of cases, respectively. Cohen’s kappa values for stage, grade and extent were respectively 0.447, 0.284, and 0.652. A multiple correspondence analysis showed high variance between ChatGPT’s staging and the variables affecting the stage (64.08%) and low variance between ChatGPT’s grading and the variables affecting the grade (42.71%). Conclusions: The present performance of ChatGPT in the classification of periodontitis exhibited a reasonable level. However, it is expected that additional improvements would increase its effectiveness and broaden its range of functionalities (NCT05926999). Clinical relevance: Despite ChatGPT's current limitations in accurately classifying periodontitis, it is important to note that the model has not been specifically trained for this task. However, it is expected that with additional improvements, the effectiveness and capabilities of ChatGPT might be enhanced.

Academic procrastination (AP), a common behavior among students, is influenced by factors such as fear of failure, task aversiveness, anxiety, and stress. Self-generated stress (SGS) refers to the subjective experience of tension that occurs in response to an existing situation. Individuals often overestimate the difficulty and attainability of a task, resulting in increased stress and extended stress duration. SGS can worsen mental and physical health problems, including bruxism. Therefore, this study investigates the possible underlying connections by examining the relationship between AP, SGS, and self-reported (SR) bruxism in medical and dental students. The present cross-sectional study was undertaken among a sample of 379 students pursuing medical and dentistry degrees in Turkey. The questionnaire, including the Academic Procrastination Scale-Short Form, SR bruxism Scale, and SGS Scale, was administered to the students. According to the responses given to the SR bruxism Scale, 3 types of SR bruxism groups were created as sleep, awake, and compound. A comparative analysis was conducted on AP, SGS, and SR bruxism among medical and dental students. Furthermore, the associations among AP, SGS, and SR bruxism were assessed using the utilization of correlation tables and regression analysis. The prevalence of awake bruxism was higher among dental students (46.6%) compared to medical students (34.7%), suggesting a faculty-dependent trend. A significant positive correlation was observed between SGS and AP (r = 0.212, p < 0.001). Median AP values differed significantly based on awake bruxism (p = 0.007) and compound bruxism (p = 0.013), but not sleep bruxism (p = 0.352). SGS exhibited statistically significant differences across all SR bruxism types: sleep (p < 0.001), awake (p < 0.001), and compound (p < 0.001). Multiple regression analysis identified SGS as a significant risk factor for SR bruxism, with a one-unit increase in stress score elevating the SR bruxism risk by 1.086 times. This study revealed that self-generated stress significantly increases the risk for the development of SR bruxism. A significant relationship was found between academic procrastination and self-generated stress, and this relationship was particularly associated with an increased risk for the development of compound and awake bruxism. Stress-reducing therapy methods should be recommended for students to both increase academic success by reducing academic procrastination and prevent the negative effects of stress-related SR bruxism.

Objective: Periodontitis and type 2 diabetes mellitus (T2DM) are chronic diseases with a well-established bidirectional relationship. Recent studies have focused on salivary biomarkers to better understand their shared inflammatory mechanisms. This study aimed to assess salivary levels of asprosin, interleukin (IL) -39, IL-40, and IL-1β in patients with and without periodontitis and/or diabetes. Materials and methods: Eighty-eight participants were classified into four groups based on periodontal and diabetic status. Unstimulated saliva samples were collected and analyzed by ELISA. In addition to clinical periodontal parameters, body mass index (BMI) and glycated hemoglobin (HbA1c) levels were recorded. Results: Salivary asprosin and IL-1β levels were elevated in periodontitis groups regardless of T2DM status and showed positive correlations with BMI. IL-39 showed no significant findings. All biomarkers, with the exception of IL-39, were positively correlated with clinical periodontal parameters. IL-40 was elevated in both diabetic and non-diabetic patients with periodontitis. Conclusions: Elevated salivary asprosin, IL-40, and IL-1β levels in diabetic individuals with periodontitis may indicate their involvement in the inflammatory interactions linking metabolic dysfunction and periodontal tissue breakdown. IL-39 showed limited utility. These findings enhance understanding of salivary inflammatory patterns in diabetes-associated periodontitis, although the saliva-only, cross-sectional design warrants cautious interpretation. Clinical relevance: The identification of salivary asprosin, IL-40, and IL-1β as potential indicators of periodontal and metabolic inflammation suggests that saliva-based testing could support non-invasive screening and monitoring in patients with diabetes and periodontitis. Clinical trial number: NCT06735313.

Periodontal diseases could cause halitosis and may impair taste and smell. While non-surgical periodontal therapy is known to reduce halitosis, its effects on taste and smell are less studied. This study aims to investigate the factors influencing self-perceived halitosis, taste, and smell, as well as the changes in these perceptions after periodontal therapy. A total of 183 participants were divided into three groups: 61 patients with periodontitis, 61 with gingivitis, and 61 who were gingivally healthy. Periodontal parameters and self-perceived halitosis, taste, and smell were evaluated at baseline and four weeks after non-surgical periodontal treatment using a visual analog scale (VAS). Robust regression analysis was used to assess independent variables influencing baseline VAS ratings. The periodontitis group had the lowest taste perception and the highest self-perceived halitosis scores (p < 0.05). Taste perception was negatively associated with ≥ 4 mm pockets (p = 0.002). A positive relationship was also observed between the plaque index and self-perceived halitosis (p = 0.030). Post-treatment, taste perception improved significantly in all groups (p < 0.05), in parallel with improvements in periodontal parameters. Additionally, self-perceived halitosis showed a significant decrease (p < 0.05). The improvement in smell perception was statistically significant in the gingivally healthy and periodontitis groups (p < 0.05). Periodontal disease may contribute to the development of chemosensory disorders. While the main goal of periodontal treatment is disease management, it can also improve taste and smell function. Oral hygiene practices play an essential role in the development of these improvements. However, further research is needed on the subject. The study was registered as “Investigation of Halitosis, Taste, and Smell in Terms of Periodontal Condition Stated by Patients and Periodontal Diagnosis by Dentists, and Then Evaluation of Change Before and After Treatment” with the registration number NCT06063460 (13/09/2023) at https://www.clinicaltrials.gov Protocol Registration and Results System. This clinical trial was registered prior to participant recruitment on ClinicalTrials.gov (NCT06063460,13.09.2023).