External Assessment Research Articles

Philosophical and logical study of the European Renaissance Historiography in the XIV-XVI centuries

The article outlines and systematizes the fundamental historical and philosophical concepts of European, in particular, the Italian, Renaissance of the XIV–XVI centuries. The paper emphasizes the principles and structure of the research methodology of the Renaissance philosophy historiography. It clarifies the logic and principal priorities of the internal concepts and external assessments of the Renaissance and offers the author’s opinion on the study of the Renaissance philosophy historiography.

An advanced sensitive detection method for internal and external exposures assessment of para-phenylenediamines and their quinone derivatives based on hierarchical pore-structured nitro-microporous organic networks

para-Phenylenediamines (PPDs) and their quinone derivatives (PPD-Qs) are emerging pollutants that are associated with neurotoxicity, reproductive toxicity, and genetic toxicity, raising significant public health concerns globally. In this study, a novel hierarchical pore-structured nitro-microporous organic networks (NO2-MONs) characterized by multiple adsorption interactions and rapid mass transfer rates was synthesized. Model fitting and adsorption experiments confirmed diverse interaction mechanisms, including pore effects, hydrogen bonding, π-π stacking, and hydrophobic interactions. Utilizing these advantages, NO2-MONs-coated solid-phase microextraction fibers facilitated the effective extraction of trace PPDs and PPD-Qs. Subsequently, a highly sensitive analytical approach was developed for detecting trace amounts of PPDs and PPD-Qs using GC-MS/MS analysis. This method significantly improved upon existing techniques, addressing the low accuracy associated with current semi-quantitative methods and enhancing sensitivity by two orders of magnitude. Notably, the achieved detection limits ranged from 0.38 to 2.6 pg mL−1, with recovery rates between 75.7 % and 117.5 % (RSD ≤ 9.8 %). This proposed approach provides technical support for investigating concentration-dose-effect relationships related to the environmental presence of PPDs and PDD-Qs, facilitating the development of relevant regulatory standards.

'TLDetect': AI-Based Application for Detection and Correction of Anomalous TLD Glow Curves.

This research reviews a novel artificial intelligence (AI)-based application called TLDetect, which filters and classifies anomalous glow curves (GCs) of thermoluminescent dosimeters (TLDs). Until recently, GC review and correction in the lab were performed using an old in-house software, which uses the Microsoft Access database and allows the laboratory technician to manually review and correct almost all GCs without any filtering. The newly developed application TLDetect uses a modern SQL database and filters out only the necessary GCs for technician review. TLDetect first uses an artificial neural network (ANN) model to filter out all regular GCs. Afterwards, it automatically classifies the rest of the GCs into five different anomaly classes. These five classes are defined by the typical patterns of GCs, i.e., high noise at either low or high temperature channels, untypical GC width (either wide or narrow), shifted GCs whether to the low or to the high temperatures, spikes, and a last class that contains all other unclassified anomalies. By this automatic filtering and classification, the algorithm substantially reduces the amount of the technician's time spent reviewing the GCs and makes the external dosimetry laboratory dose assessment process more repeatable, more accurate, and faster. Moreover, a database of the class anomalies distribution over time of GCs is saved along with all their relevant statistics, which can later assist with preliminary diagnosis of TLD reader hardware issues.

Low-Field Magnetic Resonance Imaging for the Detection of Medial Meniscal Lesions in Cranial Cruciate-Deficient Stifle Joints in Dogs.

The aim of this study was to substantiate the ability of noninvasive low-field magnetic resonance imaging (lfMRI) to accurately depict meniscal lesions, thereby establishing lfMRI as a viable option for the diagnosis of meniscal pathology. This was a prospective, controlled study. In each of 57 stifle joints, 5 predefined lfMRI sequences using a 0.25 T lfMRI were used. The scans were evaluated by an experienced surgeon (DECVS) and through external assessment by a radiologist (DECVDI). The patients were divided into two groups depending on their meniscal status after lfMRI. Dogs (Group 1; n = 33) without lfMRI evidence of medial meniscal pathology underwent TPLO, whereas concurrent craniomedial mini-arthrotomy was performed in patients with suspected meniscal lesions (Group 2; n = 24). Gait assessment was performed 6 weeks later and 6 months postoperatively. Cohen's kappa value of 0.8571 corresponds to a high level of agreement between the observers. A significant correlation was found between preoperative lameness severity and intraoperative meniscal damage (p < 0.04). The results showed a highly significant correlation between meniscal injuries on MRI and intraoperative findings (p < 0.0001). LfMRI appears to be a noninvasive, reliable imaging tool for the detection of medial meniscal lesions in cranial cruciate-deficient stifle joints, aiming to avoid (minimally) invasive diagnostics in healthy menisci.

Predicting Student Performance Using Educational Data Mining: A Review

Educational Data Mining (EDM) strategies facilitate the efficient and in-depth analysis of student data. EDM provides useful insights into comprehending student learning patterns and identifying factors that influence academic success. This review aims to evaluate the efficacy of classification algorithms popularly explored in EDM for predicting student performance and identifying common trends in existing EDM research. The review follows a systematic approach, relevant research articles have been cited following an inclusion and exclusion criteria to ensure the selection of studies that specifically address the use of EDM techniques for predicting student academic achievement. According to the review findings, most researchers have utilized the features of cumulative grade point average, internal and external assessment, and demographic information to predict student performance. The most common techniques in EDM for predicting students’ performance are Naïve Bayes and Decision Trees. The review also focuses on the potential for bias, key examination of challenges, and possible future directions in the field. In the context of student performance prediction, ethical considerations regarding privacy, data handling, and the interpretation of results are also identified

Latin Challenges: Perceptions, Policy and Curriculum for Excellence. Barriers to Teaching Latin in Scottish Schools

Over the last decade, the number of Scottish learners presented for National Qualification exams in Latin has declined. There are a number of factors contributing to this decline. Some of these relate to the study of Classics generally, such as perceptions around the subject being socially and academically elitist, while others are specific to the study of Latin and the challenges arising from it being a language. Some factors are relevant to the situation of Latin internationally, for example the desire for education which is more explicitly relevant to the demands of modern societies, while others relate to specific features of the Scottish education system. Examination of the impact of Scotland’s Curriculum for Excellence (CfE) and of the Scottish Language Learning policy on education will give context to the current position of Latin within primary and secondary schools. Significant institutional support for the learning of Gaelic and Mandarin and the challenges currently facing all language learning in Scotland in the senior phase of formal schooling must also be considered in any discussion of the barriers to the provision of Latin. The decision of the Scottish Qualifications Authority (SQA), the body responsible for the accreditation of educational awards in Scotland, to cease offering a National Qualification in Ancient Greek in 2015 was based on low uptake of the subject, making the provision of external assessment no longer viable. It is, therefore, crucial to examine the current status of Latin within Scottish education and to determine the reasons for its decline in order to better understand how to address those issues, where possible, so as to secure the sustainability of the subject in the curriculum as well as its accreditation through National Qualifications.

Deep learning-driven ultrasound-assisted diagnosis: optimizing GallScopeNet for precise identification of biliary atresia.

Biliary atresia (BA) is a severe congenital biliary developmental abnormality threatening neonatal health. Traditional diagnostic methods rely heavily on experienced radiologists, making the process time-consuming and prone to variability. The application of deep learning for the automated diagnosis of BA remains underexplored. This study introduces GallScopeNet, a deep learning model designed to improve diagnostic efficiency and accuracy through innovative architecture and advanced feature extraction techniques. The model utilizes data from a carefully constructed dataset of gallbladder ultrasound images. A dataset comprising thousands of ultrasound images was employed, with the majority used for training and validation and a subset reserved for external testing. The model's performance was evaluated using five-fold cross-validation and external assessment, employing metrics such as accuracy and the area under the receiver operating characteristic curve (AUC), compared against clinical diagnostic standards. GallScopeNet demonstrated exceptional performance in distinguishing BA from non-BA cases. In the external test dataset, GallScopeNet achieved an accuracy of 81.21% and an AUC of 0.85, indicating strong diagnostic capabilities. The results highlighted the model's ability to maintain high classification performance, reducing misdiagnosis and missed diagnosis. GallScopeNet effectively differentiates between BA and non-BA images, demonstrating significant potential and reliability for early diagnosis. The system's high efficiency and accuracy suggest it could serve as a valuable diagnostic tool in clinical settings, providing substantial technical support for improving diagnostic workflows.

Критериальное поле и диагностические инструменты определения эмоционального благополучия детей дошкольного возраста в семье и образовательной организации

&lt;p&gt;The article is devoted to the important problem of identification of the emotional well-being of preschool children in the family and educational organization. Basing on the analysis of existing approaches to this problem in Russian psychological and pedagogical science, the authors have identified the criteria field of the concept of "emotional well-being", which is a complex level structure of interrelated criteria and indicators: emotional background (manifestation of emotions), the ability to control emotions (emotional stability) and emotional perception of situations (emotional attitude to situations and events). Such manifestations in the behavior and activities of a child, depending on the conditions of his life in a family or educational organization, are negative or positive. The complexity of the concept of "emotional well-being" and the criteria field of its identification in childhood requires an integrated approach to the development of diagnostic tools. The authors announced a developed set of diagnostic tools, which includes methods that allow to record external indicators of the emotional state of a child, to identify the child's internal emotional attitude to current or predicted events, as well as to obtain an external indirect assessment of the child's subjective state and the child's subjective attitude to situations in the family and educational organization. It is noted that the announced methods can be used in the practice of preschool education in order to support psychological and pedagogical development of preschool children including various target groups, as well as in the targeted psychological assistance of families with preschool children.&lt;/p&gt;

Workflow improvement and financial gain after integration of high-throughput sample processing system with flow cytometer in a high-volume pathology laboratory: Results from a prospective comparative study using Lean principles

BackgroundHighly efficient clinical laboratories are essential for monitoring many human illnesses. Ampath Laboratory Services, the largest pathology lab in South Africa, analyzes large numbers of peripheral blood samples for CD4 levels yearly. ObjectiveTo assess productivity and quality of a newer integrated automated solution, the BD FACSDuet™ Sample Preparation System/BD FACSLyric™ Flow Cytometer using conventional assessment methods and Lean concepts. Materials and methodsThis prospective study compared the performance of the BD FACS™ Sample Preparation Assistant [SPA] III and BD FACSCanto™ II Flow Cytometer with the newly introduced integrated system (BD FACSDuet™/BD FACSLyric™). They were validated for accuracy, precision, and external quality assessment. Process mapping and Lean assessment helped identify steps leading to waste. An economic model was developed to characterize workflow and economic impact associated with total daily hands-on time, processing time, and reworks. ResultsStrong linear correlation was present between both systems. Precision and accuracy studies revealed that all coefficient of variation (CV)% values were below 20 % of allowable limits. External proficiency assessments were within limits. The fully automated workflow of BD FACSDuet™/BD FACSLyric™ permitted better consistency with significantly shorter processing time and batch processing and reduced operator interventions. Lean assessment identified defects with motion, over-processing, waiting, and non-utilized talent. Significant reductions in hands-on and total daily processing time that could increase daily specimen testing efficiency and fewer reworks were associated with the BD FACSDuet™/BD FACSLyric™. Lean improvements translated to significant economic savings associated with operator costs and unnecessary reagent consumption. ConclusionBD FACSDuet™/BD FACSLyric™ is an accurate, reliable, and cost-effective fully automated system for high-volume flow cytometry labs that perform T-cell enumeration using a single-platform and single-tube approach.

Tick control prevents carcass condemnations in lambs caused by Anaplasma ovis.

Ovine anaplasmosis is causing relevant economic losses in Spain due to icteric carcass condemnation in lambs. Anaplasma ovis infection occurs through grazing sheep that transfer ticks to their offspring. This study compared the efficacy of deltamethrin and cypermethrin pour-on treatments for tick control. A total of 250 PCR A. ovis-positive ewes and their offspring were divided into 5 groups. Group A (50 ewes/50 lambs) was kept as an untreated control group. In groups B (50/50) and C (45/93), the lambs were treated with deltamethrin pour-on and cypermethrin pour-on, respectively, one week after birth. In groups D (50/75) and E (51/68), the ewes were treated with cypermethrin pour-on and deltamethrin pour-on one week before the estimated parturition. External parasite assessment and A. ovis PCR were conducted before treatment and at 21 and 42 days post-treatment. Ewes were checked weekly for tick-detection until weaning. Lamb carcasses were examined at the slaughterhouse. Riphicephalus sanguineus sensu lato ticks were found in ewes throughout the study, with only one tick found in a control group lamb. Three lambs tested positive for A. ovis during the trial, with one condemnation at the abattoir due to jaundice. However, no significant differences were observed between treatment groups. Overall, a significant decrease in infected animals and condemned carcasses was observed compared to previous years, suggesting that deltamethrin and cypermethrin prevent A. ovis transmission from dams to lambs. Further studies are needed to confirm synthetic pyrethroids' effectiveness in controlling tick infestation and averting A. ovis transmission to lambs.

Advances in wearable electronics for monitoring human organs: Bridging external and internal health assessments

Devices used for diagnosing disease are often large, expensive, and require operation by trained professionals, which can result in delayed diagnosis and missed opportunities for timely treatment. However, wearable devices are being recognized as a new approach to overcoming these difficulties, as they are small, affordable, and easy to use. Recent advancements in wearable technology have made monitoring information possible from the surface of organs like the skin and eyes, enabling accurate diagnosis of the user's internal status. In this review, we categorize the body's organs into external (e.g., eyes, oral cavity, neck, and skin) and internal (e.g., heart, brain, lung, stomach, and bladder) organ systems and introduce recent developments in the materials and designs of wearable electronics, including electrochemical and electrophysiological sensors applied to each organ system. Further, we explore recent innovations in wearable electronics for monitoring of deep internal organs, such as the heart, brain, and nervous system, using ultrasound, electrical impedance tomography, and temporal interference stimulation. The review also addresses the current challenges in wearable technology and explores future directions to enhance the effectiveness and applicability of these devices in medical diagnostics. This paper establishes a framework for correlating the design and functionality of wearable electronics with the physiological characteristics and requirements of various organ systems.

CURRENT STATE OF TRAINING PROGRAM EVALUATION ACCORDING TO VIETNAMESE STANDARDS - SOLUTIONS TO ENSURE TRAINING PROGRAM QUALITY

The evaluation of training programs is usually conducted in at least two stages: self-assessment and external assessment, based on domestic and international standards. The evaluation results according to these standards, especially Vietnam's set of standards, serve as a foundation for developing improvement solutions to enhance the quality of training programs. The research team selected a method based on the statistical analysis of external evaluation results of training programs according to the standards issued in Circular 04/2016/TT-BGDĐT by the Ministry of Education and Training. From there, they forecast and manage existing issues as a basis for proposing 17 groups of quality assurance solutions for training programs, meeting the minimum requirements of stakeholders. Keywords: Quality; training program; external assessment; education quality assessment.

Boys’ Underperformance, Teacher Scoring Bias and Unbalanced Teacher Market in Rural Philippines

This study exploits a natural experimental setting in rural Philippines to investigate the reversed gender disparity in education. We compare two rating systems implemented in the Philippines, observing that boys’ underperformance persists in evaluations by teachers who know the examinees. Conversely, this bias is not evident in external assessments where the examinees’ attributes are concealed from evaluators. Our empirical analyses identify two underlying channels. The first is the disparity rooted in a stereotype against male students. The second is teacher-rated scores capturing a broader range of diligence aspects, such as attendance, where male students are likely to underperform. This bias is also amplified by the likelihood of student–teacher gender (mis)match, reflecting teacher labour-market conditions in developing countries. This study has limitations due to the relatively small sample to generalise and determine which mechanism prevails over the other. Nonetheless, we emphasise that gender disparity is driven by stereotypes (i.e. being male or diligent towards a specific gender) of one gender over the other.

Inactivated LMBV vaccine with nano‑aluminum adjuvant provided immune protection in largemouth bass (Micropterus Salmoides)

Largemouth bass ranavirus (LMBV) has caused severe economic losses in largemouth bass (Micropterus salmoides) aquaculture. Vaccination is acknowledged as an efficient strategy to prevent viral diseases in fish. In this study, an inactivated virus vaccine with nano‑aluminum adjuvant was successfully developed to protect largemouth bass effectively against LMBV. LMBV was inactivated by formalin and mixed with nano‑aluminum adjuvant under moderate agitation. The nano‑aluminum hydroxide adjuvant showed a cuboidal structure under the scanning transmission electron microscope (STEM), scanning electron microscope (SEM), and transmission electron microscope (TEM), and LMBV antigen-adsorbing rate was 39.49 % superior to those of micro‑aluminum hydroxide and IMS1312 adjuvants. The results from both external and internal damage assessments indicated that the nano‑aluminum vaccine had good safety for Micropterus salmoides. The relative percentage of survival was 80 % with a booster immunization in largemouth bass. The expression levels of immune-related genes such as TNF-α, IFN, and IL-8 were significantly up-regulated in the spleen of fish immunized by nano‑aluminum adjuvant vaccine compared to those in inactivated LMBV and control groups. The neutralizing antibody in nano‑aluminum adjuvant vaccine group showed higher abundance and lasted for a longer period. Lymphocytes from the head kidney of largemouth bass in the nano‑aluminum adjuvant vaccine group showed stronger proliferation activity in vitro post-stimulation with ConA. Moreover, stronger migration of the dendritic cells was induced by the nano‑aluminum adjuvant vaccine. Collectively, our findings suggested that the inactivated LMBV vaccine with nano‑aluminum adjuvant could provide efficient protection for largemouth bass against LMBV infection. The nano‑aluminum adjuvant has potential application with inactivated viral vaccines in fish.

A small step toward precision dosing of caffeine in preterm infants: An external evaluation of published population pharmacokinetic models

BackgroundSeveral population pharmacokinetic (PopPK) models of caffeine in preterm infants have been published, but the extrapolation of these models to facilitate model-informed precision dosing (MIPD) in clinical practice is uncertain. This study aimed to comprehensively evaluate their predictive performance using an external, independent dataset. MethodsData used for external evaluation were based on an independent cohort of preterm infants. Currently available PopPK models for caffeine in preterm infants were identified and re-established. Prediction- and simulation-based diagnostics were used to assess model predictability. The influence of prior information was assessed using Bayesian forecasting. Results120 plasma samples from 76 preterm infants were included in the evaluation dataset. Twelve PopPK models of caffeine in preterm infants were re-established based on our previously published study. Although two models showed superior predictive performance, none of the 12 PopPK models met all the clinical acceptance criteria of these external evaluation items. Besides, the external predictive performances of most models were unsatisfactory in prediction- and simulation-based diagnostics. Nevertheless, the application of Bayesian forecasting significantly improved the predictive performance, even with only one prior observation. ConclusionsTwo models that included the most covariates had the best predictive performance across all external assessments. Inclusion of different covariates, heterogeneity of preterm infant characteristics, and different study designs influenced predictive performance. Thorough evaluation is needed before these PopPK models can be implemented in clinical practice. The implementation of MIPD for caffeine in preterm infants could benefit from the combination of PopPK models and Bayesian forecasting as a helpful tool.

Enhancing Radiological Biomedical Natural Language Processing Tasks with Radiology-Specific Word Encodings: A Comparative Analysis of Word Embeddings Sources

Background: Machine Learning (ML)-based Biomedical Natural Language Processing (BNLP) techniques have garnered attention in radiology. However, these models typically depend on Word Encodings (WE) trained on generic datasets, as radiology-specific word libraries are limited. Objective: This study aimed to investigate the potential of radiography as a comprehensive database for generating Radiology-Specific Word Encodings (RSWE), enhancing the efficiency of BNLP tasks, especially in processing radiological texts. Methods: A systematic evaluation was conducted using WE derived from four databases: medical records, biomedical journals, Wikipedia, and news sources. Unstructured Electronic Medical Record (EMR) data from the Mayo Clinic and PubMed Central publications were used to train WE for medical-specific sources, while GloVe and Google News represented publicly available pre-trained WE for generic sources. Analytical evaluation employed medical keywords in three categories (illness, symptoms, drugs), and a 2-D graphical plot was created for 380 medical words. Numerical evaluation consisted of internal and external assessments. Results: Findings revealed that RSWE derived from EMR and PubMed Central outperformed generic WE, better capturing medical word meanings and identifying medically essential terms, aligning more closely with expert assessments. Conclusion: The study demonstrates the value of radiography as a radiology-specific resource for generating RSWE, with promising implications for improving BNLP in radiology.

Resilient-whole: A new framework for content-based second language (L2) curriculum design and teaching at college

The COVID19 pandemic, the economic crisis, and geopolitical concerns are weighing on the entire human life. Living in the era of change and uncertainty, as a second language (L2) educator and researcher, I start to think about and work on the new meaning of L2 education. In this paper, I propose a resilient-whole framework with the goal of transforming L2 curriculum into a more reactive, adaptive, diverse and inclusive, and sustainable one that possesses the power of cultivating L2 learners’ mind–body–world alignment throughout their language learning processes. A resilient mind, namely, the ability to navigate life, adapt to change, and learn through adversity, is considered to be essential to L2 learners’ success, not only in the academic fields but also in their future career and life. With a focus on resiliency, this paper demonstrates how L2 curriculum at the college level could achieve the goal by adopting a content-based language instructional approach to course design and teaching. Specifically, resiliency is discussed from the perspectives of content-language unit design, tasks, and projects for online teaching and learning, internal and external collaborations, assessment rubrics, and student advising. It is through tightening all these aspects together, with resiliency permeating into everyday practices, that the L2 classroom is further enriched and strengthened in the era of change. This article was published open access under a CC BY-ND licence: https://creativecommons.org/licenses/by-nd/4.0/ .

Optimal inputs for machine learning models in predicting total joint arthroplasty outcomes: a systematic review.

Machine learning (ML) models may offer a novel solution to reducing postoperative complication rates and improving post-surgical outcomes after total joint arthroplasty (TJA). However, the variety of different ML models that exist paired with the increasing number of potential inputs can make the implementation of this tool challenging. Therefore, we conducted a systematic review to assess the most optimal inputs of different ML models in predicting postoperative (1) medical outcomes, (2) orthopedic outcomes, and (3) patient-reported outcome measures (PROMs) after total joint arthroplasty. The PubMed, MEDLINE, EBSCOhost, and Google Scholar databases were utilized to identify all studies evaluating ML models predicting outcomes following TJA between January 1, 2000, and June 23, 2023 (PROSPERO study protocol registration: CRD42023437586). The mean risk of bias in non-randomized studies-of interventions score, was 13.8 ± 0.5. Our initial query yielded 656 articles, of which 25 articles aligned with our aims, examining over 20 machine learning models and 1,555,300 surgeries. The area under the curve (AUC), accuracy, inputs, and the importance of each input were reported. Twelve studies evaluating medical complications with 13ML models reported AUCs ranging from 0.57 to 0.997 and accuracy between 88% and 99.98%. Key predictors included age, hyper-coagulopathy, total number of diagnoses, admission month, and malnutrition. Five studies evaluating orthopedic complications with 10ML models reported AUCs from 0.49 to 0.93 and accuracy ranging from 92 to 97%, with age, BMI, CCI, AKSS scores, and height identified as key predictors. Ten studies evaluating PROMs comprising of 12 different ML models had an AUC ranging from 0.453 to 0.97 ranked preoperative PROMs as the post-predictive input. Overall, age was the most predictive risk factor for complications post-total joint arthroplasty (TJA). These studies demonstrate the predictive capabilities of these models for anticipating complications and outcomes. Furthermore, these studies also highlight ML models' ability to identify non-classical variables not commonly considered in addition to confirming variables known to be crucial. To advance the field, forthcoming research should adhere to established guidelines for model development and training, employ industry-standard input parameters, and subject their models to external validity assessments.

Transformative Learning &amp;amp; Self-Efficacy in a world shaped by AI: Empowering students through Future Skills

Generative AI faces educational institutions in particular with an immense challenge and requires them to prepare students for unknown contexts in order not to fall behind in the race against technological progress. Teaching Future Skills as such competencies that prepare students for complex, emergent situations is a key to achieving this. This paper shows a pedagogical teaching-learning concept to promote Future Skills in higher education. With various learning materials students are supported in their personal skills development and are also prepared for the demands of the future working world through evidence-based self- and external assessments and situational and self-reflective learning. In addition to the pedagogical concept, quantitative evaluation results of the workshop on which the concept is based are presented. The Workshop was trialed with 1143 students and evaluated via questionnaire. The aim of the research design was to find out the acceptance, the reaction and learning effectiveness of the Future Skills Learning design as well as the self-efficacy of the students.

Botulinum toxin for bruxism treatment: a nationwide study among oral and maxillofacial surgeons in Germany.

This study aimed to evaluate the use of botulinum toxin (BTX) for the treatment of bruxism in oral and maxillofacial surgery in Germany. A dynamic online questionnaire comprising 7 to 25 questions was formulated to gather general and specific information regarding using BTX to treat bruxism. The questionnaire underwent internal and external assessments for validation. Subsequently, it was distributed to 906 oral and maxillofacial surgeons (OMFS) affiliated with the German Association for Oral and Maxillofacial Surgery (DGMKG). Weekly reminders were dispatched over four weeks to enhance response rates. Participation in the study was voluntary and anonymized. Descriptive methods were employed for data analysis. 107 OMFS participated in the study, yielding a response rate of 11.81%. On average, 17 patients with bruxism were per month, with 4 of these patients receiving BTX therapy. BTX administration was frequently accompanied by splints and physiotherapy (35.51% of participants). Botox® (Allergan) was the preferred BTX preparation, utilized by 40.79% and reconstituted with saline by 92.11% of participants. The masseter muscles were primarily targeted for BTX treatment (67.57% of participants), averaging 29 BTX (Allergan-) units per side. Injection points for each masseter muscle typically amounted to six per side, preferred by 30.67% of participants. Follow-up assessments post-BTX treatment were conducted regularly, predominantly after four weeks, by 36% of participants. In 8% of cases, additional BTX injections were necessary due to inadequate outcomes. Side effects were reported in 4% of cases, commonly manifesting as a non-disturbing reduction in bite force. Most participating OMFS (61.84%) using BTX for bruxism therapy regarded bruxism treatment with BTX as evidence-based. Notably, 97.37% of respondents expressed their willingness to recommend BTX-based bruxism treatment to their colleagues. Overall, the efficacy of BTX therapy for bruxism was rated as good (53.95%) and very good (40.79%). The use of BTX for the management of bruxism among OMFS in Germany has demonstrated efficacy. Substantial variances in certain facets of bruxism treatment employing BTX have been observed. Additional research endeavors are warranted to comprehensively investigate distinct elements of BTX therapy for bruxism, including the optimal dosage of BTX units and the precise localization of injection sites across various muscles.