Tool For Reduction Research Articles

Two decades of electronic nicotine delivery systems discussions in Türkiye: AI-driven text analysis

Abstract Background As Turkiye ranks second in cigarette consumption in Europe, the increasing shift to Electronic Nicotine Delivery Systems (ENDS) presents emerging public health concerns. This study aims to introduce a hybrid approach by combining AI-driven large language models (LLM) with traditional thematic analysis to explore 20 years of ENDS discourse on Eksisozluk, a popular Turkish social media platform, using the Health Belief Model (HBM). Methods This mixed-methods study, analyzed 5,907 comments about ENDS from 2003 to 2023, sourced from a renowned online user-generated forum in Turkiye. Utilizing an LLM, comments were first categorized according to the HBM. Subsequently, the LLM’s generative capabilities were employed to identify sub-themes through thematic analysis. A temporal analysis was then conducted to examine the evolving trends over the 20-year span. Results ENDS popularity peaked in 2017, dipped during the COVID-19 pandemic, and later rebounded. Using an LLM with 70% accuracy, validated by a manually labeled sample, we discerned two main themes: perceived benefits (40.5%) - primarily using ENDS as for harm reduction (32.8%) and smoking cessation tools (21.6%)- and perceived barriers (27.7%), with cost and accessibility (21.9%) being the most significant throughout the period. Initially, the focus was on cessation, but shifted to harm reduction post-pandemic. Conclusions Our study indicates that consumers in Turkiye initially used ENDS for cessation but recognized their ineffectiveness over time. During the COVID-19 pandemic, usage shifted towards harm reduction, likely influenced by the rhetoric of the Tobacco Industry. Despite a national ban, ENDS remain widely used in Turkiye. Key messages • Turkiye should develop a social marketing strategy to raise awareness that ENDS is not a valid tool for harm reduction. • Although the sale of ENDS is prohibited in Turkiye, enforcement of bans should be strengthened as part of an effective control strategy.

Data-assisted non-intrusive model reduction for forced nonlinear finite elements models

AbstractSpectral submanifolds (SSMs) have emerged as accurate and predictive model reduction tools for dynamical systems defined either by equations or data sets. While finite-elements (FE) models belong to the equation-based class of problems, their implementations in commercial solvers do not generally provide information on the nonlinearities required for the analytical construction of SSMs. Here, we overcome this limitation by developing a data-driven construction of SSM-reduced models from a small number of unforced FE simulations. We then use these models to predict the forced response of the FE model without performing any costly forced simulation. This approach yields accurate forced response predictions even in the presence of internal resonances or quasi-periodic forcing, as we illustrate on several FE models. Our examples range from simple structures, such as beams and shells, to more complex geometries, such as a micro-resonator model containing more than a million degrees of freedom. In the latter case, our algorithm predicts accurate forced response curves in a small fraction of the time it takes to verify just a few points on those curves by simulating the full forced-response.

Mindfulness Exercises Reduce Acute Physiologic Stress Among Female Clinicians.

Approximately 50% of clinicians experience excessive emotional, physical, and mental stress, with repercussions across the entire medical system. Mindfulness exercises may mitigate this excessive stress. Heart rate variability (HRV) is an objective stress measure that can quantify which mindfulness exercises provide the greatest stress reduction. To define the impact of specific mindfulness exercises on HRV, a surrogate for physiologic stress, and the relationship between physiologic (HRV) and subjective stress measured by the State-Trait Anxiety Inventory during a one-day mindfulness workshop. This was a prospective observational pilot study performed at a quaternary children's hospital with diverse subspecialists of pediatric nurses, nurse practitioners, and physicians. Our primary outcome measure was change in HRV from baseline during three mindfulness exercises. The grounding, deep breathing, and body scan exercises all produced statistically significant changes in HRV among our 13 female participants. The body scan exercise produced statistically significant changes in all studied HRV parameters compared with baseline. We observed significant increases in Root Mean Square of Successive Differences between normal heartbeats (p = 0.026), high frequency (p ≤ 0.001), and the parasympathetic nervous system index (p ≤ 0.001) reflecting increased parasympathetic tone (e.g., relaxation), whereas sd 2/sd 1 ratio (p ≤ 0.001) and the stress index (p = 0.004) were decreased reflecting sympathetic withdrawal (e.g., decreased stress). Subjective stress decreased after 1-day mindfulness training (44.6 to 27.2) (p < 0.001). Individuals with the largest decrease in subjective stress also had the most improvement in HRV during the body scan exercise. Clinician stress levels (HRV) improved after participating in grounding, deep breathing, and body scan meditations, which may highlight their importance as stress reduction tools for clinicians. Monitoring of HRV during mindfulness exercises may provide deeper understanding of which specific exercises produce the greatest physiologic stress reduction for individual participants and the trend of these changes over time.

Spatially Explicit Life Cycle Global Warming and Eutrophication Potentials of Confined Dairy Production in the Contiguous US

Assessing the spatially explicit life cycle environmental impacts of livestock production systems is critical for understanding the spatial heterogeneity of environmental releases and devising spatially targeted remediation strategies. This study presents the first spatially explicit assessment on life cycle global warming and eutrophication potentials of confined dairy production at a county scale in the contiguous US. The Environmental Policy Integrated Climate model was used to estimate greenhouse gases (GHGs), NH3, and aqueous nutrient releases of feed production. The Greenhouse gases, Regulated Emissions, and Energy use in Transportation model and Commodity Flow Survey were used to assess GHGs and NH3 from feed transportation. Emission-factor-based approaches were primarily used to calculate GHGs from enteric fermentation, and GHGs, NH3, and aqueous nutrient releases from manure management. Characterization factors reported by the Intergovernmental Panel on Climate Change and Tool for Reduction and Assessment of Chemicals and other Environmental Impacts model were used to compute global warming and eutrophication potentials, respectively. The analyses revealed that life cycle global warming and eutrophication potentials of confined dairy production presented significant spatial heterogeneity among the US counties. For example, the life cycle global warming potential ranged from 462 kg CO2-eq/head to 14,189 kg CO2-eq/head. Surprisingly, sourcing feed locally cannot effectively reduce life cycle global warming and eutrophication potentials of confined dairy production. The feed supply scenarios with the lowest life cycle environmental impacts depend on the life cycle environmental impacts of feed production, geographic locations of confined dairy production, and specific impact categories. In addition, installing buffer strips in feed-producing hotspots can effectively reduce life cycle nutrient releases of confined dairy production. If 200 counties with the highest life cycle EP of corn adopt buffer strips, the reduction in life cycle EP of confined dairy production could reach 24.4%.

Lean Manufacturing en la producción de alimentos: Revisión sistemática, análisis bibliométrico y propuesta de aplicación

In a changing environment, efficiency and adaptability are essential to improve business competitiveness. The present study analyzed the application of Lean Manufacturing in food production through a review of the scientific literature based in Scopus, using the PRISMA methodology and the Vosviewer and Bibliometrix software. From the bibliometric analysis, there is evidence of an increase in research on Lean Manufacturing in food companies since 2019, with an annual growth rate of 5.96%. Brazil and Peru stand out for their high scientific production, while Brazil and Belgium stand out in terms of citations. In addition, outstanding scientific collaboration between authors from South America and Asia is observed. Three relevant clusters were identified: principles, implementation tools and practical applications of Lean Manufacturing. The thematic mapping highlighted the concepts Cost Reduction, Process Monitoring, Six Sigma and Value Stream Mapping as the most prominent topics underlining the interest and applications in the scientific literature. The implementation of Lean Manufacturing in seven steps is proposed, facilitating the use of agile tools for process optimization and waste reduction in food companies. Lean Manufacturing contributes to improve the flow of products in the supply chain and to the sustainability of small and medium enterprises. Future research should focus on adapting Lean Manufacturing to various contexts and business models in the agri-food sector.

Disentangling dynamic and stochastic modes in multivariate time series

A signal decomposition is presented that disentangles the deterministic and stochastic components of a multivariate time series. The dynamical component analysis (DyCA) algorithm is based on the assumption that an unknown set of ordinary differential equations (ODEs) describes the dynamics of the deterministic part of the signal. The algorithm is thoroughly derived and accompanied by a link to the GitHub repository containing the algorithm. The method was applied to both simulated and real-world data sets and compared to the results of principal component analysis (PCA), independent component analysis (ICA), and dynamic mode decomposition (DMD). The results demonstrate that DyCA is capable of separating the deterministic and stochastic components of the signal. Furthermore, the algorithm is able to estimate the number of linear and non-linear differential equations and to extract the corresponding amplitudes. The results demonstrate that DyCA is an effective tool for signal decomposition and dimension reduction of multivariate time series. In this regard, DyCA outperforms PCA and ICA and is on par or slightly superior to the DMD algorithm in terms of performance.

Radiographic and Clinical Outcomes of First Tarsometatarsal Joint Arthrodesis With a Biplanar Locking Plate System Versus the Modified Lapidus Technique With Crossed-Screw Fixation: A Retrospective Multicenter Comparison

Background: Hallux valgus (HV) is recognized as a triplanar deformity. A biplanar locking plate (BLP) system corrects this deformity through first tarsometatarsal joint (TMTJ) arthrodesis, with specialized reduction tools and cutting guides. Yet the optimal surgical technique and fixation construct for first TMTJ arthrodesis remains controversial. Purpose: We sought to compare the BLP system with a modified Lapidus (ML) technique with crossed-screw fixation in terms of radiographic outcomes, complications, and reoperations. Methods: In this retrospective multicenter study, we identified a series of consecutive patients who underwent first TMTJ arthrodesis for HV with either the ML procedure at institution A or the BLP system at institution B. Patients 18 years of age with a minimum of 6 months of postoperative radiographs were included. There were 130 patients, 65 in each group, including 121 women (93.8%) with a median age of 58 years and mean radiographic follow-up of 7.1 months. Data included preoperative and postoperative HV angle (HVA), intermetatarsal angle (IMA), and tibial sesamoid position (TSP), plus complications and reoperations. Statistical testing included Mann-Whitney U, Wilcoxon signed rank, Fisher exact, McNemar, and multivariable regression. Results: After adjusting for confounding variables, the BLP system was associated with significantly greater improvements in postoperative IMA and HVA but not TSP. There were no significant differences in rates of complications (ML: 18.4%; BLP: 9.2%) or reoperations (ML: 4.6%; BLP: 7.7%). Conclusion: This retrospective multicenter review found that the BLP system was associated with greater improvement in radiographic HV parameters compared with the ML procedure using crossed-screw fixation. Clinical significance is unclear as complication and reoperation rates were similar between groups. Further study in this regard is warranted.

E-Cigarettes in Smoking Cessation and Harm Reduction: A Comprehensive Review of Recent Evidence and Potential Risks

The global prevalence of tobacco use has significantly decreased over the years, with projections indicating further decline. However, the rise of new nicotine products suggests that nicotine addiction will remain a major public health issue. Electronic cigarettes (e-cigarettes) are increasingly recognized as potential tools for smoking cessation and harm reduction. This review evaluates the safety and effectiveness of e-cigarettes both as standalone treatments and in combination with other therapies, such as nicotine replacement therapy (NRT), varenicline, cytisine, and behavioral approaches. A comprehensive analysis of studies published over the past decade, including systematic reviews, meta-analyses, and randomized controlled trials, shows that e-cigarettes can enhance smoking cessation rates and reduce tobacco-related harm. They exhibit comparable short-term efficacy to traditional treatments, with promising results in alleviating withdrawal symptoms and lowering smoking rates. However, the long-term safety and effectiveness of combining e-cigarettes with other therapies remain uncertain and require further investigation. Despite their potential benefits, e-cigarettes pose significant safety concerns, underscoring the need for ongoing research to refine cessation strategies and ensure their safe, long-term use. Rigorous evaluation and regulatory oversight are essential to optimize e-cigarettes' role in reducing tobacco harm.

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

Integrated Workflow Development for Data-Driven Neighborhood-Scale Building Performance Simulation

Abstract As urbanization intensifies, cities are key contributors to energy consumption and carbon emissions, accounting for a significant portion of global energy use and CO2 emissions. This paper introduces a systematic approach to support the development of urban projects with minimized operational carbon footprints through the integration of data-driven building performance simulation (BPS) tools in early-stage design. Emphasizing the necessity for a collaborative effort among designers, policymakers, and other stakeholders, we discuss the evolution of BPS toward incorporating data-driven tools for energy need reduction and informed decision-making. Despite the proliferation of modeling methods and data-related challenges, we present a theoretical workflow, supported by interactions with design firms in the US and European Union (EU) through interviews. This structured approach, demonstrating adaptability and scalability across urban contexts, foregrounds the potential for future data-driven integration in design practices. Grounded in theoretical concepts and preliminary real-world insights, our work emphasizes the transformation of standard activities toward data-driven processes, showcasing the crucial role of practical experience in advancing sustainable, low-carbon urban development.

The Synergistic Effect of the Carbon Emission Trading Scheme on Pollution and Carbon Reduction in China’s Power Industry

The synergistic effect of pollution and carbon reduction can alleviate the dual pressure of improving environmental quality and reducing greenhouse gas emissions in China. The carbon emission trading scheme (CETS) is a crucial market-based tool for carbon emission reduction, and understanding its synergistic impact on air pollution control is essential. Based on data from 30 provincial panels in China spanning from 2007 to 2021, we employ the difference-in-differences (DID) method to analyze the synergistic effects of the carbon emission trading plan in the power industry and its influence mechanisms are examined. We observe that the CETS significantly enhances both pollution and carbon reduction in China’s power sector, particularly demonstrating effective synergy in reducing CO2, SO2, and PM2.5 emissions. Furthermore, mechanism analysis reveals that the CETS achieves joint emission reductions by lowering energy consumption, influencing the power industry’s generation structure, promoting technological innovation among enterprises, and thereby realizing synergistic pollution and carbon reduction effects in China’s power sector. Heterogeneity analysis shows that regions with limited power facility, low electricity generation, and small economic scale exhibit the most pronounced synergistic benefits from pollution and carbon reduction efforts.

Zakat Calculation Model as a Reduction Factor for Income Tax Based on Law Number 11 of 2006 concerning the Government of Aceh

The purpose of this study is to examine the implementation of the provisions of Law Number 11 of 2006, especially Article 192 which regulates Zakat as a deduction from income tax. In this study, the researcher examines the extent to which this rule is applied in practice and attempts to evaluate the most effective zakat calculation model as a deduction from income tax. This study uses qualitative methods through interviews, observations, and documentation regarding Zakat as a deduction from tax. The research findings indicate that the implementation of the provision of zakat as a deduction from tax as regulated in Article 192 of Law Number 11 of 2006 has not been optimal in Aceh, especially in Lhokseumawe City. Although this law provides a strong legal basis, the practice in this field still refers to Law Number 23 of 2011 concerning Zakat Management. The implementation of Law Number 23 of 2011 in Lhokseumawe only focuses on the administration and distribution of zakat without providing clear guidelines in integrating zakat into the taxation system, thus inhibiting the use of zakat as a tax reduction tool.

Reflecting on the 1775 Mount Gamalama Eruption: Lessons from Indigenous Knowledge for Sustainable Development in Ternate Island, North Maluku Province, Indonesia

The volcanic Molucca islands have provided the world with their precious cloves, which have been highly valued in medicinal properties for millennia. Cloves have antibacterial, antiviral, and analgesic characteristics. They were considered silver bullet cures in medieval epidemics and the early COVID-19 pandemic response. Cloves originated from Ternate Island, the center and regional trade hub of the Molucca Islands. Its cloves had reached Babylonia 4,000 years ago, marking its importance in global trade. Qualitative research was conducted on the 1775 Mount Gamalama eruption's impact on indigenous knowledge development. To confirm the findings, I conducted field visits, key informant interviews, field observations, and a literature review. Tolire Lake is an apparent landmark in Takome village, which has a sacred white crocodile, a java plum hill, a lush forest, stunning landscapes, and an underwater mythical village. The residents are testaments to human resilience, preserving indigenous knowledge and turning it into disaster risk reduction tools. They built settlements on safer ground and designated parts areas for conservation-based tourism. In addition, modern science introduction enhances disaster early warning and alert systems and empowers indigenous communities. Understanding indigenous knowledge and wisdom is critical to building resilience and sustainability through disaster risk reduction (DRR) efforts. Indigenous knowledge produces lessons in environmental protection, social justice, and economic growth. The world can learn from indigenous and scientific knowledge combined to sustain the island, its people, and its prosperity.

Identification of organic contaminants in water and related matrices using untargeted liquid chromatography high-resolution mass spectrometry screening with MS/MS libraries

Nontargeted and suspect screening with liquid chromatography-high resolution mass spectrometry (LC-HRMS) has become an indispensable tool for quality assessment in the aquatic environment – complementary to targeted analysis of organic (micro)contaminants. An LC-HRMS method is presented, suitable for the analysis of a wide variety of water related matrices: surface water, groundwater, wastewater, sediment and sludge, including extracts from passive samplers and on-site solid phase enrichment, while focusing on the data processing aspect of the method. A field study is included to demonstrate the practical application and versatility of the whole process. HRMS/MS data were recorded following LC separation in both (ESI) positive and negative ionization modes using data dependent as well as data independent acquisition. Two vendor (Agilent's Personal Compound Database and Library and from National Institute of Standards and Technology) and one open (MassBank/EU) tandem mass spectral libraries were utilized for the identification of compounds via mass spectral match. The development of a novel software tool for parsing, grouping and reduction of MS/MS features in data files converted to mascot generic format (MGF) helped to substantially decrease the amount of time and effort needed for MS library search. While applying the method, in the course of the entire field study, 18771 detections (from 870 individual compounds) in total were recorded in 275 samples, resulting in 68.3 identified compounds per sample, on average. Among the top ten most frequently detected contaminants across all samples and sample types were pharmaceutical compounds carbamazepine, 4-acetamidoantipyrine, 4-formylaminoantipyrine, tramadol, lamotrigine and phenazone and industrial contaminants toluene-2-sulfonamide, tolytriazole, tris(2-butoxyethyl) phosphate and benzotriazole. Exploratory data analysis methods and tools enabled us to discover organic pollutant occurrence patterns within the comprehensive sets of qualitative data collected from various projects between the years 2018–2023. The results may be used as valuable inputs for future water quality monitoring programs.

Dimension reduction based on time-limited cross Gramians for bilinear systems

The cross Gramian is a useful tool in model order reduction but only applicable to square dynamical systems. Throughout this paper, time-limited cross Gramians is firstly extended to square bilinear systems that satisfies a generalized Sylvester equation, and then concepts from decentralized control are used to approximate a cross Gramian for non-square bilinear systems. In order to illustrate these cross Gramians, they are calculated efficiently based on shifted Legendre polynomials and applied to dimension reduction, which leads to a lower dimensional model by truncating the states that are associated with smaller approximate generalized Hankel singular values. In combination of the dominant subspace projection method, our reduction procedure is modified to produce a bounded-input bounded-output stable-preserved reduced model under some certain conditions. At last, the performance of numerical experiments indicates the validity of our reduction methods.

Towards integrated multi-risk reduction strategies: A catalog of flood and earthquake risk mitigation measures at the building and neighborhood scales

The integration of risk reduction measures for earthquakes and floods is a promising concept in the field of multi-hazard disaster risk reduction. However, existing studies mostly discuss it conceptually, often missing a focus on decision-making. This paper introduces a novel approach by examining synergies and trade-offs between earthquake and flood risk reduction measures. The paper first conducts a detailed analysis of selected measures for these two hazards. It then explores how these individual risk mitigation measures can be strategically integrated at the building level to maximize synergies, potentially resulting in time and cost savings. Furthermore, it discusses the limitations of current decision-making procedures for single-hazard risk mitigation selection when applied to multi-hazard risk (MHR) scenarios. To address these challenges, the study proposes new decision variables to be incorporated into existing Multi-Criteria Decision-Making frameworks with the aim of facilitating strategic risk reduction prioritization for MHR scenarios, ensuring sustainability and community resilience. To aid implementation (i.e., the phase in which the measure would be selected and tested in a series of case study buildings), a catalog of analyzed risk reduction measures is developed and provided as Supplementary Material. In future developments, this catalog will facilitate the integration of these measures into decision-support tools for evidence-based multi-hazard disaster risk reduction.

Tratamiento no farmacológico de la diabetes mellitus tipo 2. Cirugía de la obesidad. Nuevas estrategias terapéuticas

The incidence and prevalence of type 2 diabetes mellitus (DM2) and obesity are increasing globally and are closely interrelated. Obesity surgery (metabolic surgery) is a valid, effective tool for weight reduction and remission of DM2. This update describes the different surgical techniques in obesity surgery. It also lists the available evidence on the observational studies and clinical trials that have supported recommendations for this intervention in patients with DM2 and obesity. Lastly, emerging treatment strategies related to peptides involved with the incretin effect (GLP-1, GIP, and glucagon) and glucose receptor inhibitors (SGLT) are described.

Prediction of typical gas components in cigarette smoke based on transformer

Abstract Harm reduction of cigarette products has become one of the primary objectives of the entire tobacco industry. The prediction of typical gas components (TGC) and dynamic adjustment of process parameters are crucial for cigarette coke reduction and harm reduction. In this study, a deep learning (DL)-assisted TGC prediction framework is proposed to predict CO and NI simultaneously. Firstly, a large number of experimental and simulation parameters of cigarette process are collected as the data base for subsequent model construction. Then, the feature importance analysis of the process parameters was carried out by combining the mechanism of the combustion process through the mutual information method. Finally, DL models based on Multilayer Perceptron, One-Dimension Convolutional Neural Network and Transformer (TF) was developed as data-driven surrogate models to establish the mapping relationship between process parameters and TGC. The results show that TF model generalizes best and can predict TGC quickly and accurately with R2 over 0.99. This work will provide a valuable predictive and decision-making tool for cigarette harm reduction.

Effect of ESG performance on stock price crash risk: evidence from China

ABSTRACT This study investigates the impact of ESG performance on stock price crash risk using a dataset of Chinese-listed firms in the period 2015 – 2020. Empirical evidence reveals a negative association between ESG performance and stock price crash risk, particularly under high product market competition conditions. Mechanism analysis suggests that ESG performance reduces stock price crash risk by alleviating maturity mismatch and optimizing debt maturity in investment and financing, but only when market competition is intense. These findings align with the resource dependence theory, indicating that strong ESG performance enhances a firm’s ability to acquire resources and mitigate stock price crash risk in highly competitive environments. Consequently, ESG performance not only augments a firm’s competitive advantage but also serves as a risk reduction tool, enabling investors to construct more sustainable investment portfolios.

Metal artifact reduction tool and mA levels impact on the diagnosis of fracture extension in endodontically treated teeth using cone-beam CT.

To evaluate the influence of different levels of metal artifact reduction (MAR) tool and milliamperage (mA) on the diagnosis of fracture extension in endodontically treated teeth using cone beam CT (CBCT). Ten maxillary premolars were endodontically treated and positioned in the empty sockets of a human maxilla covered with wax. CBCT acquisitions were performed using the Eagle Edge device (Dabi Atlante, Brazil) adjusted to 120 kVp, FOV of 4 × 6cm, exposure time of 24s and voxel size of 0.2mm in 8 different conditions with different MAR (1, 2 and 3) and mA (3.2 and 6.3) levels. Crown-root fractures were simulated in the universal testing machine, and CBCT images were acquired again. Five radiologists evaluated the presence and extension of fractures with a 5-point scale. Statistical analysis was performed by analysis of variance, Tukey and Kappa test (α = 0.05). Although different mA levels did not significantly (p > 0.05) affect the diagnosis values for fracture presence and extension, when evaluated the different levels of MAR, AUC and sensitivity showed significantly higher values (p < 0.05) for MAR 0 using 6.3mA and kappa agreement showed significantly higher values (p < 0.05) for MAR 0 and 2 using 6.3mA. Although mA levels do not have a diagnostic effect when isolating the MAR level; in 6.3mA, MAR 0 and 2 can positively influence the diagnosis of fracture extension in endodontically treated teeth using CBCT. The isolate evaluation of dental fracture presence can overlook diagnostics error of its extension.