The effect of climate changes on incidence of benign paroxysmal positional vertigo; a systematic review with meta-analysis of 16144 patients.

BACKGROUND: There were controversial findings in terms of the association between the incidence of Benign Paroxysmal Positional Vertigo (BPPV) and climate changes, so the current systematic review plus meta-analysis is designed to discover this possible relationship. METHODS: Web of science, PubMed, Scopus, Google Scholar, Embase, and Cochrane library were systematically searched up to August 2023. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Problem/Population, Intervention, Comparison, and Outcome (PICO) guidelines were used. Two authors independently reviewed the eligible articles and assessed the quality of them. RESULTS: In total, 15 studies including 16144 patients met the inclusion criteria. Ten studies reported the relation of BPPV to monthly mean temperature, 7 to monthly average humidity, 4 to monthly average rainfall, 6 to monthly sunlight time, and 2 to average solar radiation. The incidence of BPPV was associated significantly with atmospheric pressure (P: 0.003) and rainfall (P: 0.017). However, there was not any statistically significant correlation between incidence of BPPV and humidity, sunlight time, temperature, and solar radiation level (P > 0.05). CONCLUSIONS: The incidence of BPPV was higher in cold months of a year in both northern hemisphere and southern hemisphere countries. Although it can be because of negative correlation with temperature, the current meta-analysis did not find any statistically significant negative correlation with temperature. In addition, the incidence of BPPV was associated significantly with atmospheric pressure (positive correlation) and rainfall (negative correlation).

Exploring the Association of Serum Uric Acid Levels with Benign Paroxysmal Positional Vertigo: A Systematic Review and Meta-Analysis.

Objectives: The role of uric acid in pathogenesis of benign paroxysmal positional vertigo (BPPV) is not fully understood. It is aimed to assess the serum uric acid levels in BPPV patients compared to healthy controls. Study design: Systematic review and meta-analysis. Methods: Web of science, PubMed, Scopus, Google Scholar, Embase, Medline, and Cochrane library were systematically searched. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Problem/Population, Intervention, Comparison, and Outcome (PICO) guidelines were used. Results: In total, 20 studies including 3967 participants met the inclusion criteria. Ten studies (50%) reported higher uric acid (UA) levels in BPPV patients, 4 studies (20%) indicated lower UA levels in BPPV patients, while 6 studies (30%) found no significant difference in UA levels between BPPV patients and healthy controls. The overall mean serum levels of UA (SMD: 0.265, [-0.163 to 0.693]) were higher in BPPV patients than control group. However, this difference was not statistically significant (P-value: .225). Conclusion: There is no significant difference in serum level of UA between BPPV patients and healthy controls. It means that serum level of UA (whether low or high) is not likely the underlying factor of development of BPPV.

Role of Early Postoperative Measurement of Copeptin in Prediction of Diabetes Insipidus Following Pituitary Surgery in Adults: a systematic review plus meta-analysis.

PURPOSE: Early diagnosis of Diabetes insipidus (DI), a complication following pituitary surgery, can avoid the catastrophic results such as lethargy or even death. Measurement of Arginine vasopressin (AVP) may help the early diagnosis, but its direct assaying is challenging. Copeptin, which is co-secreted in equimolar quantities to AVP, is suggested to be a reliable marker in prediction of post-op DI. Therefore, this systematic review plus meta-analysis aims to discover this possible role. METHODS: Google Scholar, PubMed, Scopus, Web of Science, Embase, and Cochrane library were systematically searched up to August 2024. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Problem/Population, Intervention, Comparison, and Outcome (PICO) guidelines were used. Two authors independently reviewed the eligible articles and assessed the quality of them. A meta-analysis was conducted to assess the discriminative performance of copeptin. RESULTS: In total, 8 cohort studies including 1255 participants met the inclusion criteria. The median copeptin levels were significantly lower in DI groups compared to non-DI groups in all included studies (P < 0.005). Meta-analysis of AUCs demonstrated that early measurement of copeptin level had an accuracy of 0.791 (SE: 0.0198, 95% CI: 0.752 to 0.830), which was statistically significant (P < 0.001). CONCLUSION: Copeptin level was significantly lower in DI patients than in non-DI patients who underwent pituitary surgery. Early measurement, as soon as possible (from the first hour to 48 hours after the operation), of copeptin after pituitary surgeries has good, but not excellent, accuracy to exclude post-op DI.

Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis.

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.

Effect of Needle Insertion Angle on Pain During Labial Infiltration Anesthesia of the Anterior Maxilla: A Randomized Clinical Trial.

OBJECTIVES: This study aimed to assess the effect of needle insertion angle on pain during labial infiltration anesthesia in the anterior maxillary region. MATERIAL AND METHODS: In this parallel-design randomized clinical trial, participants were randomly assigned to four groups for labial infiltration anesthesia of the anterior maxilla. Local anesthesia was performed with needle orientation parallel to the longitudinal axis of the tooth using a conventional syringe (Syringe-0), needle at α angle with a conventional syringe (Syringe-α), computer-controlled local anesthetic delivery (CCLAD) device parallel to the longitudinal axis of the tooth (CCLAD-0), and CCLAD at α angle (CCLAD-α). The heart rate (HR), blood pressure (BP), and respiratory rate (RR) of participants were measured before needle insertion, immediately after needle insertion, and immediately after the injection by a vital signs monitor. The level of pain experienced by participants was quantified using a numerical rating scale (NRS). Data were analyzed by repeated-measures ANOVA and regression models (α = 0.05). RESULTS: Thirty-six participants aged from 21 to 60 years, with a mean age of 35.36 years were recruited. The mean pain scores were 7.44, 4.67, 2.89, and 0.67 in groups Syringe-0, Syringe-α, CCLAD-0, and CCLAD-α, respectively (p < 0.001). Age and sex had no significant effect on pain scores (p = 0.914 and p = 0.702, respectively). The four groups had no significant difference in vital signs (p > 0.05). CONCLUSIONS: Injection at an α angle and the application of CCLAD can be used in clinical practice to decrease the pain experienced by participants during labial infiltration anesthesia of the anterior maxilla. TRIAL REGISTRATION: Iranian Registry of Clinical Trials: IRCT20230719058849N1.

Performance of ChatGPT 3.5 and 4 on U.S. dental examinations: the INBDE, ADAT, and DAT.

Purpose: Recent advancements in artificial intelligence (AI), particularly tools such as ChatGPT developed by OpenAI, a U.S.-based AI research organization, have transformed the healthcare and education sectors. This study investigated the effectiveness of ChatGPT in answering dentistry exam questions, demonstrating its potential to enhance professional practice and patient care. Materials and Methods: This study assessed the performance of ChatGPT 3.5 and 4 on U.S. dental exams - specifically, the Integrated National Board Dental Examination (INBDE), Dental Admission Test (DAT), and Advanced Dental Admission Test (ADAT) - excluding image-based questions. Using customized prompts, ChatGPT's answers were evaluated against official answer sheets. Results: ChatGPT 3.5 and 4 were tested with 253 questions from the INBDE, ADAT, and DAT exams. For the INBDE, both versions achieved 80% accuracy in knowledge-based questions and 66-69% in case history questions. In ADAT, they scored 66-83% in knowledge-based and 76% in case history questions. ChatGPT 4 excelled on the DAT, with 94% accuracy in knowledge-based questions, 57% in mathematical analysis items, and 100% in comprehension questions, surpassing ChatGPT 3.5's rates of 83%, 31%, and 82%, respectively. The difference was significant for knowledge-based questions (P=0.009). Both versions showed similar patterns in incorrect responses. Conclusion: Both ChatGPT 3.5 and 4 effectively handled knowledge-based, case history, and comprehension questions, with ChatGPT 4 being more reliable and surpassing the performance of 3.5. ChatGPT 4's perfect score in comprehension questions underscores its trainability in specific subjects. However, both versions exhibited weaker performance in mathematical analysis, suggesting this as an area for improvement.