Variation In Decisions Research Articles

Antimicrobial Prescription Practices and Stewardship in Washington State Small and Mixed Animal Veterinary Medicine.

Judicious antimicrobial use is essential for the continued treatment of infections in small and mixed animal veterinary medicine. To better support Washington (WA) State veterinarians in antimicrobial stewardship, we surveyed licensed small and mixed animal veterinarians and led group conversations regarding antimicrobial prescription practices. Survey questions included demographic information, factors influencing prescription practices and clinical cases. Responses were summarised and logistic regressions were performed to identify factors associated with antibiotic treatment choices. Group conversations, led by a licensed veterinarian, focused on resource gaps for veterinarians, management of clinical scenarios and interpretation of minimum inhibitory concentrations (MICs) and breakpoints. A systematic qualitative analysis of conversation transcripts identified key themes such as common barriers to stewardship. Among 53 responses to clinical scenarios, veterinarians selected the most appropriate treatment choice, according to a veterinary microbiologist, 62% of the time. Variability was observed in culture and susceptibility practices and antibiotic choices. Survey respondents reported an influence of the client ability to medicate (92%), considerations of resistance (91%), client finances (75%) and availability of antimicrobials (75%) on their prescription decisions. There were no significant associations between opinions about contributing factors to antimicrobial resistance (AMR) or guidelines used and treatment choices in clinical scenarios. Among 15 veterinarians interviewed in group conversations, a systematic qualitative analysis of conversation transcripts revealed key themes, including reliance on human medicine as a resource and a lack of support for veterinarians in interpreting MICs and breakpoints. The variability in veterinary antibiotic treatment decisions in this study suggests a need for further dissemination of standardised antimicrobial stewardship resources for veterinarians. Client-related challenges and the cost of culture and susceptibility are major barriers to stewardship. To address these barriers, it is necessary to provide standardised, easy-to-access guidance for veterinarians in interpreting MICs and breakpoints, as well as develop antimicrobial use resources for clients.

Product aesthetics and consumer buying decision in the cosmetic sub-sector in Nigeria

In the recent modern marketing age, there is huge competition of product atheistic due to changing life style of the consumers and the improvement in cosmetic products. Every organization focuses on the packaging positioning of products because that is the main tool of stimulating consumer buying decision. The objective of this paper was to examine the effect of product aesthetics on the consumer buying decision. This research identified the relationship between the dependent and independent variables. This survey research consists on the primary data which were collected through the distribution of the questionnaire and SPSS software was b used for the purpose of the analysis. This study consists on the response of the 100 respondents and tested the reliability of the model. Findings shows that the dimensions of product atheistic such as product information and product packaging have positive effect on consumer buying decision. Results also shows that the dimensions of product packaging explain the variability of consumer buying decision of cosmetic brands in selected super supermarkets. The study recommended that management of supermarkets should engage the services of professionals who are knowledgeable about customer reactions towards product information and texts in order to continuously develop and improve on their packaging so as to boost their consumer purchase decision. Keywords: Product Aesthetics, Product Information, Product Packaging, Consumer and Buying Decision.

Carbon and Energy Footprinting across Archetypes for U.S. Maple Syrup Production.

The production of maple syrup from sap requires extensive processing, which has traditionally led to significant energy inputs and greenhouse gas (GHG) emissions per gallon produced. Technology advancements, e.g., vacuum tubing sap collection systems, reverse osmosis (RO), and electric evaporators have changed the way syrup is produced, resulting in widespread variability in processing equipment and sugar-making operational decisions. This paper evaluates these complex operations through a cradle-to-retail gate carbon footprinting model and by capturing variability in a series of producer archetypes. By isolating energy and emissions impacts, we find that implementing RO has the largest reduction effect on energy (54-77%) and emissions (57-82%), depending on both production size and evaporator fuel (wood, fuel-oil, or electricity). Results also demonstrate the effect of production scale on cumulative energy demand (CED) and emissions per gallon of syrup, with small producers ranging from 333-1,425 MJ/gal and 27-118 kg CO2e/gal (61-90% biogenic on-site) for wood-fired operations and 18-65 kg CO2e/gal for oil-fired operations. Large producers ranged from 90-131 MJ and 3.5-7 kg CO2e/gal (electricity to oil-fired operations). Producers of all scales with the highest rates of electrification in their operations have the lowest GHG emissions and energy use per gallon of syrup produced.

Effective Communication Strategies for Business Analysts to Bridge the Gap between IT and Finance

As organizations depend more on technology to stay ahead of the competition, their information technology (IT) and business departments must work together closely. However, cultural differences and understanding between the specialized fields often make it hard for them to work together effectively. If these issues are fixed, they could make it easier to finish projects, stick to budgets, and agree on the company's goals. Many companies use business analysts to help people talk to each other by acting as liaisons knowledgeable in technology and finances. However, there is limited information in the literature about how analysts can connect the tasks. This study fills that gap by looking into the problems when IT and finance work together and suggesting specific steps analysts can take to improve strategic alignment. Looking at past studies shows that common differences make it hard for people to work together. IT and finance use different vocabularies for technical terms and project-based versus financial terms, leading to misunderstandings. They also set other priorities. It emphasizes speed of innovation and technical practicality, while finance emphasizes reducing risk, accuracy, and returns. There are also differences in the amount of knowledge and skill that people in IT and finance have regarding technical solution details and financial effects. They also use different ways to make decisions. IT bases judgments on technological factors, while finance assesses business impact. Besides, cultural norms diverge between the flexible and creative IT culture and the rigorous and methodical finance culture. Business analysts should utilize several focused communication strategies to close these gaps. Creating a shared vocabulary helps terms from diverse fields fit. Regular mapping and stakeholder conversations help you grasp other perspectives. Actively listening, repeating, asking questions, and summarizing shows you comprehend. Educating on decision-making variables and expertise broadens perspectives. Discussing cultural meanings and making sure individuals understand questions improves discussion. Using shared goals like increasing sales or lowering costs is another way to unite people around similar goals. By using these specific methods, business experts can help IT and finance work together despite cultural differences. Mapping and teaching regularly help keep relationships strong even as needs and staff change. By focusing on active listening, aligning vocabulary, and highlighting shared interests, you can build good working relationships necessary to get strategic benefits from technology investments.

Techno-economic analysis and optimization of a hybrid solar-wind-biomass-battery framework for the electrification of a remote area: A case study

An off-grid hybrid energy framework on the basis of wind turbines and photovoltaic panels as the primary source of energy and a biogas generator and energy storage unit as a back-up system, was considered for clean energy generation in a rural area in Semnan, Iran. This study was aimed at designing an optimized hybrid renewable framework with minimal costs and CO2 emissions and highest reliability, utilizing comprehensive modeling based on two energy management strategies. The decision-making variables in this work were the type and number of wind turbines, number of storage units, capacity of solar panels, and the capacity of biogas generator. Then variations in turbine model, inflation rate and biofuel prices and their effect on the optimal design of the hybrid framework was investigated in a range of uncertainties (0, 1, and 2.5 %). Additionally, the powerplant emissions penalty cost rate was also considered. Eventually, the optimal hybrid frameworks were economically and environmentally compared with a stand-alone diesel generator. Optimization results confirmed the superiority of the proposed hybrid wind-solar-biomass-battery system in comparison with other investigated systems. The cost of energy for the optimal design including biogas generator (22 kW), photovoltaic panels (30.7 kW), a 10 kW wind turbine, 11 batteries and an inverter (15.1 kW), while considering the inflation rate of 10 % and the uncertainty rate of 0 %, is equal to 0.201 $/kWh. Additionally, the selected optimal design emitted 97 % less CO2 annually, in comparison with the similarly-sized diesel-based energy system.

Analyzing The Impact of Online Marketing on Students' Purchase Decisions on Shopee E-Commerce in Tasikmalaya, Indonesia

The study was driven by the growing trend of online shopping among students and the need to understand how digital marketing strategies impact their purchasing decisions on e-commerce platforms like Shopee. This research aims to evaluate the influence of online marketing on the purchasing decisions of students at STIE Latifah Mubarokiyah Suryalaya, in Tasikmalaya, Indonesia, specifically using the Shopee platform. Online marketing facilitates the transaction of goods and services via the Internet, providing the convenience of shopping without time and space constraints and allowing consumers to select products based on their preferences. The study employs a descriptive quantitative approach, involving 77 respondents selected through purposive random sampling. Data collection includes both primary and secondary sources, which are analyzed using simple regression, correlation analysis, determination analysis, and hypothesis testing. The findings reveal that online marketing strategies significantly influence students' purchasing behaviour on e-commerce platforms, with a correlation coefficient of 0.694 and a determination coefficient of 48.1%. This indicates that nearly half of the variability in student purchase decisions can be attributed to online marketing strategies. Moreover, the study offers new insights into how specific online marketing tactics, such as targeted advertisements and social media promotions, uniquely influence student preferences and purchasing patterns. These findings highlight the importance of personalized marketing approaches in shaping consumer behaviour. The study also suggests that further research should explore additional factors influencing consumer behaviour in e-commerce to achieve a more comprehensive understanding.

Hospital and Physician Variability in Revascularization Decisions and Outcomes for Patients With 3-Vessel and Left Main Coronary Artery Disease: A Population-Based Cohort Study.

Hospital- and physician-level variation for selection of percutaneous coronary intervention versus coronary artery bypass grafting (CABG) for patients with coronary artery disease has been associated with outcome differences. However, most studies excluded patients treated medically. From 2010 to 2019, adults with 3-vessel or left main coronary artery disease at 3 hospitals (A, B, C) in Alberta, Canada, were categorized by treatment with medical therapy, percutaneous coronary intervention, or CABG. Multilevel regression models determined the proportion of variation in treatment attributable to patient, physician, and hospital factors, and survival models assessed outcomes including death and major adverse cardiovascular events over 5 years. Of 22 580 patients (mean age, 67 years; 80% men): 6677 (29%) received medical management, 9171 (41%) percutaneous coronary intervention, and 6732 (30%) CABG. Hospital factors accounted for 10.8% of treatment variation. In adjusted models (site A as reference), patients at sites B and C had 49% (95% CI, 44%-53%) and 43% (95% CI, 37%-49%) lower rates of medical therapy, respectively, and 31% (95% CI, 24%-38%) and 32% (95% CI, 24%-40%) lower rates of CABG. During 5.0 years median follow-up, 3287 (14.6%) patients died, with no intersite mortality differences. There were no between-site differences in acute coronary syndromes or stroke; patients at sites B and C had 24% lower risk (95% CI, 13%-34% and 11%-35%, respectively) of heart failure hospitalization. Hospital-level variation in selection of percutaneous coronary intervention, CABG, or medical therapy for patients with complex coronary artery disease was not associated with differences in 5-year mortality rates. Research and quality improvement initiatives comparing revascularization practices should include medically managed patients.

The Influence of Digital Platform Quality and Security on Decision and Satisfaction through Trust in the DANA Digital Wallet

This research reveals several significant findings related to the factors influencing the use of the Dana digital wallet platform. The study aims to examine the impact of digital platform quality, security on decision-making, and user satisfaction through trust in the Dana digital wallet. The research was conducted among users of the Dana digital wallet application, with 166 respondents as the sample. The research method employed a quantitative approach, specifically a causal associative approach, with research instruments in the form of a Likert scale questionnaire ranging from 1 to 5. Sampling was carried out using a non-probability incidental sampling technique. The analytical approach used SEM-PLS, utilizing Smart-PLS version 4.0. The research results demonstrate that the variables of digital platform quality, security, and trust have a significant direct impact on decision-making. The user decision-making variable has the most dominant direct influence, accounting for 85.6% of user satisfaction. Furthermore, digital platform quality and security significantly influence trust directly. Additionally, digital platform quality and security also have a significant indirect impact on decision-making through trust as a mediating variable.

What influences ambulance clinician decisions to pre-alert emergency departments: a qualitative exploration of pre-alert practice in UK ambulance services and emergency departments

BackgroundAmbulance clinicians use pre-alerts to inform receiving hospitals of the imminent arrival of a time-critical patient considered to require immediate attention, enabling the receiving emergency department (ED) or other clinical...

Entrepreneurs’ Variability in Decision-Making and Downstream Consequences for Behavioral Proactivity

Entrepreneurs’ Variability in Decision-Making and Downstream Consequences for Behavioral Proactivity

Primary Elbow Arthroplasty in the Management of Complex Distal Humerus Fractures.

Introduction Complex distal humerus fractures pose significant challenges in orthopedic surgery, especially when traditional open reduction and internal fixation (ORIF) is not feasible. Primary elbow arthroplasty has emerged as an alternative treatment option for these fractures, but its application remains limited. This study aimed to evaluate the functional outcomes, patient selection criteria, and follow-up results of primary elbow arthroplasty in the management of complex distal humerus fractures. Methods A retrospective review was conducted on 15 patients who underwent primary elbow arthroplasty for Orthopaedic Trauma Association (OTA) type C distal humerus fractures between 2017 and 2023 at our institution. Inclusion criteria were patients aged 18 years or older who were offered either total elbow or hemiarthroplasty for acute complex distal humerus fracture. Data were collected from patient medical records, including demographic information, fracture classification, surgical details, and postoperative follow-up. Functional outcomes were assessed using the Oxford Elbow Score (OES) and Mayo Elbow Performance Score (MEPS). Complications were documented, and descriptive statistics were used to summarise the findings. Results The mean age of the patients was 71.8 years (IQR 17 years), with 12 females and three males. The mean time to surgery was 14.7 days post-injury (IQR: 12 days). The mean follow-up duration was 52 weeks (range: 8-234 weeks, IQR: 27 weeks) and variability was noted. The mean flexion-extension arc at the final follow-up was 93° (IQR: 32.5°). The mean OES was 46 (IQR: 22), and the mean MEPS was 75 (IQR: 37), indicating good to excellent functional outcomes. Scores for two patients were not available due to dementia. Reported complications included one case of ulnar sensory symptoms and one case requiring metalwork removal following olecranon osteotomy. Conclusion Primary elbow arthroplasty provides a viable treatment option for complex distal humerus fractures, demonstrating significant functional improvements and high patient satisfaction. However, the variability in follow-up and subjective decision-making underscores the need for standardized protocols. Future multicenter, prospective studies with larger cohorts and standardized follow-up protocols are recommended to confirm these findings and optimize patient care.

The Complex Interplay of Variables in Extubation Decision-Making Following Pediatric Cardiac Surgery: A Narrative Review.

Pediatric cardiac surgery poses significant challenges in developing countries, where a considerable number of children require intervention for congenital heart disease (CHD). The utilization of endotracheal intubation and anesthesia is pivotal in conducting surgical or angiography procedures on patients with CHD exhibiting diverse anatomical and hemodynamic characteristics. The decision to extubate pediatric patients following cardiac surgery remains a crucial element of postoperative care. This article explores the complexities surrounding extubation decision-making in this population, emphasizing the critical role of surgical, physiological, and postoperative factors. Various preoperative and intraoperative factors influence the timing of extubation. Early extubation is increasingly prevalent, offering benefits like reduced length of stay and minimized drug exposure. Multidisciplinary collaboration and protocol-driven strategies contribute to improved extubation outcomes, emphasizing the need for a comprehensive approach in pediatric cardiac surgery. Future research can focus on the implementation and efficacy of standardized extubation procedures involving collaboration among healthcare experts.

How to Define and Meet Blood Pressure Targets After Traumatic Brain Injury: A Narrative Review

BackgroundTraumatic brain injury (TBI) poses a significant challenge to healthcare providers, necessitating meticulous management of hemodynamic parameters to optimize patient outcomes. This article delves into the critical task of defining and meeting continuous arterial blood pressure (ABP) and cerebral perfusion pressure (CPP) targets in the context of severe TBI in neurocritical care settings.MethodsWe narratively reviewed existing literature, clinical guidelines, and emerging technologies to propose a comprehensive approach that integrates real-time monitoring, individualized cerebral perfusion target setting, and dynamic interventions.ResultsOur findings emphasize the need for personalized hemodynamic management, considering the heterogeneity of patients with TBI and the evolving nature of their condition. We describe the latest advancements in monitoring technologies, such as autoregulation-guided ABP/CPP treatment, which enable a more nuanced understanding of cerebral perfusion dynamics. By incorporating these tools into a proactive monitoring strategy, clinicians can tailor interventions to optimize ABP/CPP and mitigate secondary brain injury.DiscussionChallenges in this field include the lack of standardized protocols for interpreting multimodal neuromonitoring data, potential variability in clinical decision-making, understanding the role of cardiac output, and the need for specialized expertise and customized software to have individualized ABP/CPP targets regularly available. The patient outcome benefit of monitoring-guided ABP/CPP target definitions still needs to be proven in patients with TBI.ConclusionsWe recommend that the TBI community take proactive steps to translate the potential benefits of personalized ABP/CPP targets, which have been implemented in certain centers, into a standardized and clinically validated reality through randomized controlled trials.

Pengaruh Green Marketing dan Green Consumerism Terhadap Keputusan Pembelian Produk Ramah Lingkungan

The issue of plastic waste in Indonesia has reached alarming levels with annual production reaching 64 million tons, the majority of which ends up in the oceans, making Indonesia the second largest contributor to global marine plastic pollution after China. Increasing awareness and reducing daily waste generation are crucial to protecting the environment from the serious impacts of plastic's slow decomposition. This research employed accidental sampling with 20 to 30 respondents to investigate the influence of green marketing and green consumerism on purchasing decisions of environmentally friendly products. Data analysis included data exploration to identify patterns and trends, and Ordinary Least Squares (OLS) regression testing to evaluate the linear relationship between independent variables (green marketing and green consumerism) and the dependent variable (purchase decision). The results of multiple linear regression analysis indicate that the Green Consumerism variable (X2) has a positive regression coefficient of 0.260, although it is not statistically significant (p = 0.148), suggesting that consumer adoption of environmentally friendly behavior marginally contributes to purchasing decisions of environmentally friendly products. However, the low Adjusted R Square (0.031) indicates that the model only explains approximately 3.1% of the variability in purchase decisions. Therefore, companies need to consider other factors such as price and product quality, and integrate more holistic marketing strategies to enhance consumer preference for environmentally friendly products. Further research using more sensitive analytical methods could provide deeper insights to support more effective decision-making in dynamic markets.

Litigation in access to universal health coverage for children and adolescents in Brazil.

Brazil's Unified Health System (SUS) ensures universal, equitable, and excellent quality health coverage for all. The broad right to health, supported by the Constitution, has led to excessive litigation in the public sector. This has negatively impacted the financial stability of SUS, created inequality in children and adolescents' access to healthcare, and affected communication between the healthcare system and the judiciary. The enactment of Law Number 13.655 on 25 April 2018, proposed significant changes in judicial decisions. This study aimed to investigate decision-making changes in health litigation involving children and adolescents following the implementation of the new normative model. The study is cross-sectional, analyzing 3753 national judgment documents from all State Courts of Brazil, available on their respective websites from 2014 to 2020. It compares regional legal decisions before and after the promulgation of Law Number 13.655/2018. Data tabulation, statistical analysis, textual analysis, coding, and counting of significant units in the collected documents were performed. The results of data cross-referencing are presented in tables and diagrams. The majority (96.86%) of legal claims (3635 cases) received partial or total provision of what was prescribed by the physician. The Judiciary predominantly handled these cases individually. The analysis indicates that the decisions made did not adhere to the norms established in 2018. Regional heterogeneity in health litigation was observed, and there was no significant variability in decisions during the studied period, even after the implementation of the new normative paradigm in 2018. Technical-scientific support was undervalued by the magistrates. Prioritizing litigants undermines equity in access to Universal Health Coverage for children and adolescents.

Surgeon perceptions of personality as an influencing factor on anastomotic decision-making: A qualitative analysis.

Surgeon personality is a factor influencing rectal anastomotic decision-making. However, it is unclear how or why this may be the case, or what aspects of personality are involved. The aim of this study was to investigate the views of colorectal surgeons on how their individual personality may influence variation in anastomotic decision-making. Purposive sampling was used to invite certified UK-based colorectal surgeons to participate, with individual interviews used for data collection. Participants were recruited until ongoing data review indicated no new codes were generated (i.e. data sufficiency). Data were analysed thematically following Braun and Clarke's six-step framework. Seventeen colorectal surgeons (eight female, nine male) participated. Two key themes relating to personality and decision-making were identified: (1) surgeon development and training and (2) patient-surgeon interactions, each with relevant subthemes. Surgeons described how their personality may influence patients' postoperative outcomes (e.g. decision-making, team working and communication) and potential mechanisms for how their personality may influence operative risk-taking. Following anastomotic leakage, surgeons described a disproportionate sense of guilt and responsibility. There appears to be a significant transition in responsibility from trainee to newly appointed consultant, which may be part of the 'hidden curriculum' of surgical training. Colorectal surgeons have described their perceptions of how personality traits may impact variation in decision-making and patient outcomes for the first time. Early career surgeons felt ill-prepared for the level of guilt experienced when managing complications. Surgeons appear open to personality assessment if this was through an educational lens, with the aim of improving decision-making following complications and overall performance.

An ecological assessment of decision-making under risk and ambiguity through the virtual serious game Kalliste Decision Task

Traditional methods for evaluating decision-making provide valuable insights yet may fall short in capturing the complexity of this cognitive capacity, often providing insufficient for the multifaceted nature of decisions. The Kalliste Decision Task (KDT) is introduced as a comprehensive, ecologically valid tool aimed at bridging this gap, offering a holistic perspective on decision-making. In our study, 81 participants completed KDT alongside established tasks and questionnaires, including the Mixed Gamble Task (MGT), Iowa Gambling Task (IGT), and Stimulating & Instrumental Risk Questionnaire (S&IRQ). They also completed the User Satisfaction Evaluation Questionnaire (USEQ). The results showed excellent usability, with high USEQ scores, highlighting the user-friendliness of KDT. Importantly, KDT outcomes showed significant correlations with classical decision-making variables, shedding light on participants’ risk attitudes (S&IRQ), rule-based decision-making (MGT), and performance in ambiguous contexts (IGT). Moreover, hierarchical clustering analysis of KDT scores categorized participants into three distinct profiles, revealing significant differences between them on classical measures. The findings highlight KDT as a valuable tool for assessing decision-making, addressing limitations of traditional methods, and offering a comprehensive, ecologically valid approach that aligns with the complexity and heterogeneity of real-world decision-making, advancing research and providing insights for understanding and assessing decision-making across multiple domains.

How Healthcare Providers Decide on a Referral Location in Telephone Triage: A Cross-sectional Study.

Approximately 25% of patients that present to the emergency department (ED) do so after contact with a healthcare professional. Many of these patients could be effectively managed in non-ED ambulatory settings. Aligning patients with safe and appropriate outpatient care has the potential to improve ED overcrowding, patient experience, outcomes, and costs. Little is understood about how healthcare providers approach triage decision-making and what factors influence their choices. To evaluate how providers think about patient triage, and what factors influence their decision-making when triaging patient calls. Cross-sectional survey-based study in which participants make triage decisions for hypothetical clinical scenarios. Healthcare providers in the specialties of internal medicine, family medicine, or emergency medicine within a large integrated healthcare system in the Southeast. Differences in individual training and practice characteristics were used to compare observed differences in triage outcomes. Free-response data were evaluated to identify themes and factors affecting triage decisions. Out of 72 total participants, substantial variability in triage decision-making was observed among all patient cases. Attending physicians triaged 1.4 fewer cases to ED care compared with resident physicians (p < 0.001, 95% CI 0.62-2.1). Academic attendings demonstrated a trend toward fewer cases to ED care compared with community attendings (0.61, p = 0.188, 95% CI - 0.31-1.5). Qualitative data highlighted the complex considerations in provider triage and led to the development of a novel conceptual model to describe the cognitive triage process and the main influencing factors. Triage decision-making for healthcare providers is influenced by many factors related to clinical resources, care coordination, patient factors, and clinician factors. The complex considerations involved yield variability in triage decisions that is largely unexplained by descriptive physician factors.

ATP dependence of decision-making capacity in a fine-grained model of gene regulatory networks

Cellular decision-making is fundamental to life, from developmental biology to environmental responses and antimicrobial resistance. Many regulatory processes that drive cellular decisions rely on gene expression, which requires energy in the form of ATP. As even genetically identical cells can have dramatically different ATP levels, bioenergetic status can be an important source of variability in cellular decision-making. Existing studies have investigated this energy dependence but often use coarse-grained modelling approaches (which are not always readily connected to the underlying molecular processes of gene regulation). Here, we use a fine-grained mathematical model of gene expression in a two-gene decision-making regulatory network to explore cellular decision-making capacity as energy availability varies. We simulate both a deterministic model, to explore the emergence of different cell fate attractors as ATP levels vary, and a stochastic case to explore how ATP influences the noisy dynamics of stochastic cell decision-making. Higher energy levels typically support increased decision-making capacity (higher numbers of, and more separated, cell states that can be selected), and the fine-grained modelling reveals some differences in behaviour from previous coarse-grained modelling approaches.

Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment.

The extraction decision significantly affects the treatment process and outcome. Therefore, it is crucial to make this decision with a more objective and standardized method. The objectives of this study were (1) to identify the best-performing model among seven machine learning (ML) models, which will standardize the extraction decision and serve as a guide for inexperienced clinicians, and (2) to determine the important variables for the extraction decision. This study included 1000 patients who received orthodontic treatment with or without extraction (500 extraction and 500 non-extraction). The success criteria of the study were the decisions made by the four experienced orthodontists. Seven ML models were trained using 36 variables; including demographic information, cephalometric and model measurements. First, the extraction decision was performed, and then the extraction type was identified. Accuracy and area under the curve (AUC) of the receiver operating characteristics (ROC) curve were used to measure the success of ML models. The Stacking Classifier model, which consists of Gradient Boosted Trees, Support Vector Machine, and Random Forest models, showed the highest performance in extraction decision with 91.2% AUC. The most important features determining extraction decision were maxillary and mandibular arch length discrepancy, Wits Appraisal, and ANS-Me length. Likewise, the Stacking Classifier showed the highest performance with 76.3% accuracy in extraction type decisions. The most important variables for the extraction type decision were mandibular arch length discrepancy, Class I molar relationship, cephalometric overbite, Wits Appraisal, and L1-NB distance. The Stacking Classifier model exhibited the best performance for the extraction decision. While ML models showed a high performance in extraction decision, they could not able to achieve the same level of performance in extraction type decision.