OBJECTIVE: Temporomandibular disorder (TMD) is the term used to describe a pathology (dysfunction and pain) in the masticatory muscles and temporomandibular joint (TMJ). There is an apparent upward trend in the publication of dental research and a need to continually improve the quality of research. Therefore, this study was conducted to analyse the use of sample size and effect size calculations in a TMD randomised controlled trial.
METHODS: The period was restricted to the full 5 years, i.e., papers published in 2019, 2020, 2021, 2022, and 2023. The filter article type-\"Randomized Controlled Trial\" was used. The studies were graded on a two-level scale: 0-1. In the case of 1, sample size (SS) and effect size (ES) were calculated.
RESULTS: In the entire study sample, SS was used in 58% of studies, while ES was used in 15% of studies.
CONCLUSIONS: Quality should improve as research increases. One factor that influences quality is the level of statistics. SS and ES calculations provide a basis for understanding the results obtained by the authors. Access to formulas, online calculators and software facilitates these analyses. High-quality trials provide a solid foundation for medical progress, fostering the development of personalized therapies that provide more precise and effective treatment and increase patients\' chances of recovery. Improving the quality of TMD research, and medical research in general, helps to increase public confidence in medical advances and raises the standard of patient care.

Brain-computer interfaces (BCI) enable direct communication between the brain and a computer or other external devices. They can extend a person\'s degree of freedom by either strengthening or substituting the human peripheral working capacity. Moreover, their potential clinical applications in medical fields include rehabilitation, affective computing, communication, and control. Over the last decade, noninvasive BCI systems such as electroencephalogram (EEG) have progressed from simple statistical models to deep learning models, with performance improvement over time and enhanced computational power. However, numerous challenges pertaining to the clinical use of BCI systems remain, e.g., the lack of sufficient data to learn more possible features for robust and reliable classification. However, compared with fields such as computer vision and speech recognition, the training samples in the medical BCI field are limited as they target patients who face difficulty generating EEG data compared with healthy control. Because deep learning models incorporate several parameters, they require considerably more data than other conventional methods. Thus, deep learning models have not been thoroughly leveraged in medical BCI. This study summarizes the state-of-the-art progress of the BCI system over the last decade, highlighting critical challenges and solutions.

UNASSIGNED: Application of alveolar bone graft (ABG) in alveolar augmentation is done to prevent excessive bone resorption due to tooth extraction, missing teeth, or other diseases/conditions affecting the alveolar bone. The use of autogenous dentin-derived ABG has been considered as the composition of dentin appears to be nearly analogous to that of bone.
UNASSIGNED: This systematic review aims to assess the efficacy of dentin-derived ABG for alveolar augmentation of post-extraction sockets or other alveolar bone defects by evaluating volume gain and histomorphometric data.
UNASSIGNED: A search of systematic literature was conducted in Pubmed, Scopus, Web of Science, and Embase from database inception to October 2023. The review included both randomized controlled trials (RCT), pilot studies, clinical trials, and retrospective studies reporting on dentin-derived ABG use for alveolar augmentation.
UNASSIGNED: Overall, 298 articles were obtained from the initial search. From these articles, 21 articles met the inclusion criteria and were included for descriptive analysis. All of the studies indicated low risk of bias. Studies of dentin-derived ABG, which used bone-derived grafts as the control group, have shown significantly higher percentages of new bone formation, gain in vertical and horizontal dimensions, and less reduction in dimensions.
UNASSIGNED: Dentin-derived ABG was effective in volume maintenance, indicating promising results via histomorphometric and radiographic analysis.

UNASSIGNED: Cognitive and motor impairments are common among stroke survivors. Physical therapy is often used to improve the functional capacity of stroke survivors. However, limited adherence to rehabilitation programs is a challenge. Motivation plays a crucial role in the success of rehabilitation programs as it influences individual adherence to treatment and overall health outcomes. This review aims to identify current trends in motivational strategies used by healthcare professionals for stroke survivor rehabilitation.
UNASSIGNED: Following the framework developed by Arksey and O\'Malley, a scoping review was conducted. We performed a literature search using MEDLINE, CINAHL, the Cochrane Central Register of Controlled Trials, Nursing & Allied Health, and MedicLatina databases.
UNASSIGNED: A total of 906 papers were identified. After selecting and analyzing the articles, 17 papers were included in this review. Health professionals use various strategies to motivate stroke survivors. These approaches include establishing a therapeutic alliance, improving patients\' health literacy, defining realistic goals, fostering problem-solving skills, personalizing the rehabilitation program, showcasing success stories, utilizing persuasive techniques, offering encouragement and compliments, providing emotional support, and effectively managing symptoms.
UNASSIGNED: The knowledge gathered in this review can guide healthcare professionals in helping patients overcome barriers to rehabilitation, improve their motivation, and ultimately enhance their recovery outcomes.

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Virtual reality (VR) is an emerging method that can be used in many scenarios involving children. VR has been increasingly studied as it has become cheaper, more widely available, and of better quality. In this review of current meta-analyses on the use of VR in children in the medical setting, we examined its role in 5 broad settings where it is used to alleviate pain and anxiety as well as in therapeutic scenarios. The study scope was purposefully broad to highlight a wide range of cases. We searched the ScienceDirect, SpringerLink, Cochrane Library, PubMed, and PMC databases for meta-analyses using VR in pediatric populations in medical settings. The National Institutes of Health quality assessment tool and Quality of Reporting of Meta-analyses statement checklist were used to verify study quality. Six hundred fifty-three articles were retrieved; after the application of the inclusion and exclusion criteria, 11 remained. These meta-analyses included cerebral palsy (4 meta-analyses), attention deficit/hyperactivity disorder (2 meta-analyses), burn care (1 meta-analysis), preoperative anxiety (2 meta-analyses), and needle-involving procedures (2 meta-analyses). The meta-analyses showed broadly positive results, with VR being useful in the areas in which it was applied. This study had several limitations. The meta-analyses consistently highlighted a high level of heterogeneity, making it challenging to draw clear conclusions. Most meta-analyses across all fields yielded encouraging results. However, further studies are required to confirm these findings. Guidelines must be established for future experiments to provide a standard and uniform procedure for reducing the heterogeneity of experimental methods.

UNASSIGNED: ChatGPT has a wide range of applications in the medical field. Therefore, this review aims to define the key issues and provide a comprehensive view of the literature based on the application of ChatGPT in medicine.
UNASSIGNED: This scope follows Arksey and O\'Malley\'s five-stage framework. A comprehensive literature search of publications (30 November 2022 to 16 August 2023) was conducted. Six databases were searched and relevant references were systematically catalogued. Attention was focused on the general characteristics of the articles, their fields of application, and the advantages and disadvantages of using ChatGPT. Descriptive statistics and narrative synthesis methods were used for data analysis.
UNASSIGNED: Of the 3426 studies, 247 met the criteria for inclusion in this review. The majority of articles (31.17%) were from the United States. Editorials (43.32%) ranked first, followed by experimental studys (11.74%). The potential applications of ChatGPT in medicine are varied, with the largest number of studies (45.75%) exploring clinical practice, including assisting with clinical decision support and providing disease information and medical advice. This was followed by medical education (27.13%) and scientific research (16.19%). Particularly noteworthy in the discipline statistics were radiology, surgery and dentistry at the top of the list. However, ChatGPT in medicine also faces issues of data privacy, inaccuracy and plagiarism.
UNASSIGNED: The application of ChatGPT in medicine focuses on different disciplines and general application scenarios. ChatGPT has a paradoxical nature: it offers significant advantages, but at the same time raises great concerns about its application in healthcare settings. Therefore, it is imperative to develop theoretical frameworks that not only address its widespread use in healthcare but also facilitate a comprehensive assessment. In addition, these frameworks should contribute to the development of strict and effective guidelines and regulatory measures.

Sculpting silicon at the micro and nano scales has been game-changing to mold bulk silicon properties and expand, in turn, applications of silicon beyond electronics, namely, in photonics, sensing, medicine, and mechanics, to cite a few. Voltage- and metal-assisted chemical etching (ECE and MaCE, respectively) of silicon in acidic electrolytes have emerged over other micro and nanostructuring technologies thanks to their unique etching features. ECE and MaCE have enabled the fabrication of novel structures and devices not achievable otherwise, complementing those feasible with the deep reactive ion etching (DRIE) technology, the gold standard in silicon machining. Here, a comprehensive review of ECE and MaCE for silicon micro and nano machining is provided. The chemistry and physics ruling the dissolution of silicon are dissected and similarities and differences between ECE and MaCE are discussed showing that they are the two sides of the same coin. The processes governing the anisotropic etching of designed silicon micro and nanostructures are analyzed, and the modulation of etching profile over depth is discussed. The preparation of micro- and nanostructures with tailored optical, mechanical, and thermo(electrical) properties is then addressed, and their applications in photonics, (bio)sensing, (nano)medicine, and micromechanical systems are surveyed. Eventually, ECE and MaCE are benchmarked against DRIE, and future perspectives are highlighted.

The review considers the approach placing famous French surgeon A. Paré into more general European context of European Renaissance of XVI century and into local context of intellectual life of Paris of this period. The refutation of widespread in history of medicine opinion about strict separation of university medicine from artisan surgery in Medieval Europe is discussed.

BACKGROUND: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model.
METHODS: Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy.
RESULTS: The trained models\' micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80-78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%).
CONCLUSIONS: BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.