Preferred Model Research Articles

The Use of Socioscientific Issues in Science Lessons: A Scoping Review

Socioscientific issues represent an innovative approach within the realm of STEM education as they integrate real-world problems, promote critical thinking, and encourage interdisciplinary learning, thus preparing students to address complex societal challenges through scientific inquiry. The objective of this scoping review was to analyze the use of SSIs in science lessons. A database search of Web of Science and Scopus focused on articles published between 2013 and 2023. When applying the inclusion and exclusion criteria, a total of 106 articles were selected. The scoping review revealed a focus on socioscientific issues within high school and undergraduate curricula, particularly pertaining to environmental, genetic, and health-related concerns, as well as localized SSIs. A variety of methodological approaches, predominantly qualitative, were applied to capture the educational dynamics of integrating socioscientific issues into pedagogy. Inquiry-based learning emerges as a preferred pedagogical model, stimulating student engagement with real societal challenges. The educational resources employed encompass both conventional texts and digital tools, such as data mapping and visualization software, facilitating a multifaceted comprehension of SSIs. Pedagogical techniques are diverse, incorporating argumentation, role-playing, and digital media to enrich the teaching and learning experience. Nevertheless, the incorporation of socioscientific issues faces obstacles, including resistance to pedagogical innovation, the inherent complexity of the topics, and the demand for specialized teacher training.

Reproductive attitudes and family values of modern student youth (based on the author's sociological research)

In the context of current trends in the political agenda of Russia aimed at strengthening traditional values, supporting large families and birth rate, it seems necessary to analyze the current marital, family and reproductive attitudes of modern youth. Based on a questionnaire survey of students, data were obtained on the attitude of young people to marriage and family, desire and intentions to have children and preferred family models.

Learning Style Preferences of BSEd- Math Major Students in Mathematics of Investment

Student learning styles should affect assessment and instruction. Adapting the idea that people process information differently should guide our teaching. Much research has focused on learning style variations. Many feel that certain learning styles are better for certain subjects or expertise. This research examines students' learning styles and academic performance in Mathematics of Investment. VAK learning styles preference model correlation with BSEd Math majors' academic achievement. All first-year Visayas State University Bachelor of Secondary Education majors in Mathematics were targeted. The non-random purposive sampling method selected 27 students as respondents. Data was acquired using descriptive-survey research and Cynthia Arem's Learning Style Preference Inventory questionnaire. The Chi-Square Test compared Students' arithmetic grades to their learning styles. Results show that learning style affects student academic performance. Education-style questionnaires can help educators structure learning events in ways that are more popular or familiar to students by revealing respondents' preferences or self- beliefs. Educators should not assume that such attempts would improve student learning except to motivate volunteer effort. Knowing students' preferred learning styles can help educators overcome their tendency to treat all pupils equally and inspire them to use others.

Deductibles and health care utilization: An experiment on the role of forward-looking behavior

We investigate the effects of nonlinear deductible contracts on health utilization behavior by using a laboratory experiment in which we can control the likelihood of hitting the deductible. We also evaluate the effect of subjects receiving regular information updates on their remaining deductible. Our results show that varying the future price has a significant effect on health care consumption. At an individual level, we identify and richly characterize heterogeneity. We find fully forward-looking, fully myopic, as well as mixed types after controlling for risk preferences. We show that there is a substantial welfare loss due to a lack in forward-looking behavior. The distribution of the welfare loss are characterized and differ sharply according to the model of risk preferences adopted for normative evaluation. Potential drivers of welfare effects are investigated and have a tendency to differ across risk preference models.

Intelligent condition prediction model for bridge infrastructure based on evaluating machine learning algorithms

PurposePrediction models are essential tools for transportation agencies to forecast the condition of bridge decks based on available data, and artificial intelligence is paramount for this purpose. This study aims at proposing a bridge deck condition prediction model by assessing various classification and regression algorithms.Design/methodology/approachThe 2019 National Bridge Inventory database is considered for model development. Eight different feature selection techniques, along with their mean and frequency, are used to identify the critical features influencing deck condition ratings. Thereafter, four regression and four classification algorithms are applied to predict condition ratings based on the selected features, and their performances are evaluated and compared with respect to the mean absolute error (MAE).FindingsClassification algorithms outperform regression algorithms in predicting deck condition ratings. Due to its minimal MAE (0.369), the random forest classifier with eleven features is recommended as the preferred condition prediction model. The identified dominant features are superstructure condition, age, structural evaluation, substructure condition, inventory rating, maximum span length, deck area, average daily traffic, operating rating, deck width, and the number of spans.Practical implicationsThe proposed bridge deck condition prediction model offers a valuable tool for transportation agencies to plan maintenance and resource allocation efficiently, ultimately improving bridge safety and serviceability.Originality/valueThis study provides a detailed framework for applying machine learning in bridge condition prediction that applies to any bridge inventory database. Moreover, it uses a comprehensive dataset encompassing an entire region, broadening the model’s applicability and representation.

Six-month post-stroke review provision across southwest England: a service evaluation

Background/Aims Six-month post-stroke reviews are advocated for all stroke survivors. There is no recommended service model, with many variations in practice. The aim of this service evaluation was to describe and evaluate the 6-month post-stroke review service provision across the South West England Integrated Stroke Delivery Networks. Methods A mixed-methods service evaluation for 10 services was undertaken, consisting of: a stakeholder meeting to define the scope and review criteria; a mapping exercise to define core service features and activity data; a 12-month retrospective Sentinel Stroke National Audit Programme clinical data audit plus a 1-month prospective audit detailing identified unmet needs, actions to address needs and patient satisfaction; service provider and user guided discussions; and options appraisal synthesising data from all sources to inform service development. Results Considerable variations in service configuration, processes and outcomes existed across services. Guided discussions with 40 service users and 20 service providers revealed predominantly positive experiences. There was perceived value in the review, a key ingredient being its person-centred nature. However, some felt that the review was generic and more of a tick-box exercise. Many staff appeared unaware of differing 6-month post-stroke review models across the region and rarely used Sentinel Stroke National Audit Programme data to reflect on service outcomes or inform service development. Sharing of good practice across services appeared limited. Conclusions Data did not support a specific preferred service model. Findings suggested 6-month post-stroke reviews should not be a ‘one size fits all’ model, with flexibility in format and timing to optimise value. A knowledge sharing and evidence-based culture should be encouraged to nurture service development.

Instructional Coaching Personnel and Program Scalability

Abstract Instructional coaching is an attractive alternative to one-size-fits-all teacher training and development in part because it is purposefully differentiated: Programming is aligned to individual teachers’ needs and implemented by an individual coach. But, how much of the benefit of coaching as an instructional improvement model depends on the specific coach with whom a teacher works? Collaborating with a national teacher training and development organization, TNTP, we find substantial variability in effectiveness across coaches in terms of changes in preservice teachers’ instructional practice (roughly 0.25 to 0.3 standard deviation from our preferred sample and models). The magnitude of coach effectiveness heterogeneity is quite similar to average coaching program effects on teaching practice identified in other research. Through a set of alternative model specifications and permutation tests, we rule out the possibility that our estimates of coach effectiveness heterogeneity are driven by nonrandom sorting of coaches to teachers, at least on observable characteristics available in our data, as well as the possibility that these estimates are simply statistical noise. These findings suggest that identifying, recruiting, and supporting highly skilled coaches will be key to scaling instructional coaching programs.

Ultra‐Low Velocity Zone Beneath the Atlantic Near St. Helena

AbstractThere are various hotspots in the Atlantic Ocean, which are underlain by mantle plumes that likely cross the mantle and originate at the core‐mantle boundary. We use teleseismic core‐diffracted shear waves to look for an Ultra‐Low Velocity Zone (ULVZ) at the potential base of central Atlantic mantle plumes. Our data set shows delayed postcursory phases after the core‐diffracted shear waves. The observed patterns are consistent in frequency dependence, delay time, and scatter pattern with those caused by mega‐ULVZs previously modeled elsewhere. Synthetic modeling of a cylindrical structure on the core‐mantle boundary below St. Helena provides a good fit to the data. The preferred model is 600 km across and 20 km high, centered at approximately 15° South, 15° West, and with a 30% S‐wave velocity reduction. Significant uncertainties and trade‐offs do remain to these parameters, but a large ULVZ is needed to explain the data. The location is west of St. Helena and south of Ascension. Helium and neon isotopic systematics observed in samples from this region could point to a less‐outgassed mantle component mixed in with the dominant signature of recycled material. These observations could be explained by a contribution from the Large Low Shear Velocity Province (LLSVP). Tungsten isotopic measurements would be needed to understand whether a contribution from the mega‐ULVZ is also required at St. Helena or Ascension.

Barriers and Facilitators for Implementing Shared Decision Making in Differentiated Antiretroviral Therapy Service in Northwest Ethiopia: Implications for Policy and Practice

Background. Shared decision making (SDM) for patients enrolling in differentiated antiretroviral therapy (DART) is crucial. Empirical evidence is lacking regarding factors promoting or hindering SDM implementation in DART provision in Ethiopia. Hence, this study aimed to explore the barriers and facilitators to implementing SDM for patients enrolled in DART in Northwest Ethiopia. Methods. A qualitative descriptive study using semi-structured interviews among 17 patients and 15 providers at health facilities providing DART service was conducted. The MAXQDA version 20 software was used for inductive coding. Interviews were analyzed using thematic analysis. Results. Ten themes emerged at 4 levels related to SDM in the provision of DART: patient, provider, organizational, and health system. At the patient level, 1) trust in providers (facilitator) and 2) patient’s level of education (barrier) emerged as themes. At the provider level, 3) lack of familiarity with DART models (barrier) and 4) patient-provider relationship (barrier and facilitator) were emerged themes. At the organizational level, 5) workload (barrier) and 6) resources (barrier and facilitator) emerged as themes. At the health system level, 7) availability of DART models (facilitator), 8) not involving providers while initiating DART models (barrier), 9) other providers’ involvement (facilitator), and 10) presence of other implementing partners (barrier) emerged as themes. Conclusions. Numerous barriers and facilitators influence the implementation of SDM in the provision of DART. Based on these findings, the following steps are recommended. Providing access to patient decision aids shall be in place to assist patients in making decisions about their preferred DART models. Health care workers shall be trained, and patients shall be given education to enhance the SDM process. Policy makers and program managers shall consider the resource context (training and size of human resources and convenience of rooms) for the delivery of ART service to have an appropriate implementation of SDM in clinical practice. Highlights Shared decision making in DART is influenced by various barriers and facilitators present at the patient, provider, organizational, and health system levels. Patients need education, and health care staff need regular training to improve SDM in DART service provision. Patient access to decision support tools that aid in the selection of the preferred DART model in health facilities is critical. Policy makers and program managers shall consider the availability of adequate and trained human resources as well as provide adequate space and private rooms for SDM in the implementation of DART.

Risk and Systems Analysis for Renewable Power Generation with Environmental and Other Stressors

AbstractThe effects of climate change and water scarcity threaten the stability of critical infrastructure systems in developing regions. In particular, the interconnectedness of energy systems, natural resources, economic growth, and social welfare requires a systems‐level framework to identify scenarios which most impact these systems. This paper evaluates and quantifies infrastructure system risk, defined as the influence of scenarios on system priorities. A scenario‐based multi‐criteria preferences model assesses system component priorities for a baseline scenario as well as climate and related scenarios. The shift in priorities between the baseline and other future scenarios define scenario disruptiveness, or level of risk. The methods are demonstrated for the case of the emerging renewable energy sector of Iraq. Twenty‐five renewable energy system assets are prioritized by an assessment of system success criteria, which include economic, social, political, and climate considerations. The system prioritization is reevaluated in the case of seven disruptive scenarios relating to water scarcity, climate volatility, and social and economic shifts. This paper advances methods of the Systems Engineering Body of Knowledge (SEBoK) Part 3: Engineering and Management, by defining system risk and proposing methods for risk identification and risk analysis.

Exploring the neuropsychological basis of behavioral contagion during learning about another agent's social preferences: Evidence from an ERP study.

Social contagion is a pervasive phenomenon and an important social influence that involves the rapid dissemination (propagation) of behaviors, attitudes, emotions, or ideas from one person to another, often without conscious reflection or rational thought. This phenomenon is closely related to conformity, by which a person changes his/her original ideas and attitude and imitates certain behavior of others. Although some behavioral research has been carried out on contagion and conformity, there is very little neuropsychological understanding of these phenomena. Existing research on social influence and conformity has predominantly focused on tasks like mental rotation or rating tasks involving facial expressions, with fewer studies exploring risk preferences and temporal discounting. However, there is a notable gap in the literature when it comes to examining social influence and conformity using other‑regarding preference models derived from heterodox economics. To address this research gap, the present study investigates the neuropsychological underpinnings of social contagion by utilizing event‑related potentials (ERPs) recorded while subjects engage in mini‑dictator games. The behavioral analysis revealed that contagion had an impact on the participants' preferences, leading to a change in their choices. We observed a P300 component in the midline and right posterior during the time window of 200‑350 ms after stimulus onset, which showed a significant increase in mean amplitude when participants observed others' behavior, compared to when they made decisions based on their own preferences. Moreover, the lack of late positive potential in the time window of 500‑650 ms suggests that the presence of P300 may indicate difficulty in making decisions. In summary, by analyzing both behavioral and ERP data, this study may provide a more comprehensive understanding of the cognitive and neural processes that drive conformity and contagion behavior. Our analysis has the potential to inform policymakers in developing effective interventions for promoting positive social behaviors and reducing negative ones.

An Integrative Approach to the Study of Cognitive Abilities in a Non-Human Primate Model in a Virology Laboratory Environment.

Non-human primates, due to their similarities in immune response to humans, are the preferred model for studying infectious processes and any associated cognitive impairments. Behavioral tests are indispensable for investigating pathogenesis in neuroinfections, especially those that do not manifest with noticeable clinical symptoms, as well as in the transition to a chronic form of the disease. Modeling viral infection requires specialized experimental conditions. Our work describes techniques for investigating mnemonic functions, tiredness, attentional focus, quick-wittedness, and basic behavioral responses in primates under the assumed conditions for infections with viruses that do not have an airborne route of transmission. It also outlines approaches to the training and selection of primates for virological research, as well as analyzing gender differences in learning abilities, the impact of housing conditions on the results, and the correlation between training success and behavioral test scores. These methods will allow a more detailed study of non-human primates as a model for researching cognitive and behavioral impairments under infectious and immune stress, as well as the design of less energy-intensive experiments for evaluating the efficacy and safety of therapeutic and prophylactic strategies at early stages of infection.

How Does the Introduction of Shared Ride-Sourcing Services Affect Demand for Existing Modes for Non-Commuting Trips? Evidence from a Joint RP-SP Study in Metro Vancouver

The introduction and subsequent growth of ride-sourcing services have been found to affect the use of existing modes of travel. Although prior studies have explored the impacts of these services as a whole, relatively little work has been done to explore the relationship between shared ride-sourcing and existing modes of travel. Given the potential for shared ride-sourcing to help mitigate the negative externalities associated with ride-sourcing, understanding the factors influencing the use of these services and their relationship with existing modes can inform efforts to help ensure that this potential is realized. This study uses data from a web-based survey of Metro Vancouver residents to estimate a joint revealed preference–stated preference (RP–SP) model of mode choices for non-commuting trips. The model is then applied to explore the potential impacts of shared ride-sourcing on the demand for existing modes. To the authors’ knowledge, this is the first study to use a joint RP–SP model to explore the potential impacts of shared ride-sourcing on the demand for existing modes. The results suggest these services can affect the demand for exclusive ride-sourcing and attract demand from more sustainable modes such as public transit and active modes. This information can be used to help inform policies that help ensure that the benefits of shared ride-sourcing are realized. Shared ride-sourcing use can be encouraged by increasing the difference between the cost of exclusive and shared services; however, limiting the impacts of these services on the demand for more sustainable modes is also important.

The Effect of Probiotic Supplementation on Morphine Preference in Rats: Possible Antioxidative/Oxidative Signaling Pathway

Background: Many studies have revealed various aspects of the relationship between the stomach and the brain, but there has been no systematic study of the role of gut microbiota in drug addiction to date. This investigation is based on the novel concept that gut dysbiosis plays an important role in addiction disorders. Probiotic supplements (PBS) are thought to be a useful therapy method for opiate addiction and substance misuse. Therefore, it is postulated that changes in gut flora can play a significant part in the addiction process. Objectives: The aim of this study was to observe whether PBS, through the gut-brain axis in drug addiction, can decrease morphine (Mor) tendency in rats by affecting oxidative stress (OS) markers, using a conditioned place preference (CPP) paradigm. Methods: Forty-two rats (weight 180 - 200 g) were divided into six groups (n = 7 in each). The effective dose of morphine (Mor) was 7.5 mg/kg. Two probiotic supplements were used: Prodigest (Prd) containing Lactobacillus acidophilus, Bifidobacterium bifidum, and Bifidobacterium longum, and Prokid (Prk) containing Lactobacillus acidophilus, Bifidobacterium lactis, Lactobacillus rhamnosus, and Bifidobacterium bifidum. These were administered via gavage for 14 days. The animals were given PBS after each conditioning session in the conditioned place preference (CPP) model. The animals' locomotor activity was then evaluated using an open field apparatus. The brain was tested for malondialdehyde (MDA), nitric oxide (NO), catalase (CAT), superoxide dismutase (SOD), total antioxidant capacity (TAC), and thiol levels. Results: Only the Prd group, but not the Prk group, significantly increased the time spent in the Mor chamber (the location preference index) during the acquisition phase (P < 0.05). The anti-oxidation/oxidation profiles (MDA, NO, CAT, SOD, TAC, and thiol) were altered by PBS. Conclusions: Given the elevated location preference index generated by Mor, it may be argued that PBS influences mood and behavior via antioxidative/oxidative signaling, thereby potentiating the effect of Mor.

Leptomeningeal metastases from solid tumors: A Society for Neuro-Oncology and American Society of Clinical Oncology consensus review on clinical management and future directions.

Leptomeningeal metastases (LM) are increasingly becoming recognized as a treatable, yet generally incurable, complication of advanced cancer. As modern cancer therapeutics have prolonged the lives of patients with metastatic cancer, specifically in patients with parenchymal brain metastases, treatment options, and clinical research protocols for patients with LM from solid tumors have similarly evolved to improve survival within specific populations. Recent expansions in clinical investigation, early diagnosis, and drug development have given rise to new unanswered questions. These include leptomeningeal metastasis biology and preferred animal modeling, epidemiology in the modern cancer population, ensuring validation and accessibility of newer leptomeningeal metastasis diagnostics, best clinical practices with multimodality treatment options, clinical trial design and standardization of response assessments, and avenues worthy of further research. An international group of multi-disciplinary experts in the research and management of LM, supported by the Society for Neuro-Oncology and American Society of Clinical Oncology, were assembled to reach a consensus opinion on these pressing topics and provide a roadmap for future directions. Our hope is that these recommendations will accelerate collaboration and progress in the field of LM and serve as a platform for further discussion and patient advocacy.

Mode selection for component mode synthesis with guaranteed assembly accuracy

In this work, a modular approach is introduced to select the most important eigenmodes for each component of a composed structural dynamics system to obtain the required accuracy of the reduced-order assembly model. To enable the use of models of complex (structural) dynamical systems in engineering practice, e.g., in a design, optimization and/or control context, the complexity of the models needs to be reduced. When the model consist of an assembly of multiple interconnected structural components, component mode synthesis is often the preferred model reduction method. The standard approach to component mode synthesis for such system is to select the eigenmodes of a component that are most important to accurately model the dynamic behaviour of this component in a certain frequency range of interest. However, often, a more relevant goal is to obtain, in this frequency range, an accurate model of the assembly. In the proposed approach, accuracy requirements on the level of the assembly are translated to accuracy requirements on component level, by employing techniques from the field of systems and control. With these component-level requirements, the eigenmodes that are most important to accurately model the dynamic behaviour of the assembly can be selected in a modular fashion. We demonstrate with two structural dynamics benchmark systems that this method based on assembly accuracy allows for a computationally efficient selection of eigenmodes that (1) guarantees satisfaction of the assembly accuracy requirements and (2) results in most cases in reduced-order models of significantly lower order with respect to the industrial standard approach in which component eigenmodes are selected using a frequency criterion.

Aspect-enhanced Explainable Recommendation with Multi-modal Contrastive Learning

Explainable recommender systems ( ERS ) aim to enhance users’ trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the recommendations, fostering a sense of confidence and reliability in the system’s outputs. Generally, the explanations are presented in a familiar and intuitive way, which is in the form of natural language, thus enhancing their accessibility to users. Recently, there has been an increasing focus on leveraging reviews as a valuable source of rich information in both modeling user-item preferences and generating textual interpretations, which can be performed simultaneously in a multi-task framework. Despite the progress made in these review-based recommendation systems, the integration of implicit feedback derived from user-item interactions and user-written text reviews has yet to be fully explored. To fill this gap, we propose a model named SERMON (A s pect-enhanced E xplainable R ecommendation with M ulti-modal C o ntrast Lear n ing). Our model explores the application of multimodal contrastive learning to facilitate reciprocal learning across two modalities, thereby enhancing the modeling of user preferences. Moreover, our model incorporates the aspect information extracted from the review, which provides two significant enhancements to our tasks. Firstly, the quality of the generated explanations is improved by incorporating the aspect characteristics into the explanations generated by a pre-trained model with controlled textual generation ability. Secondly, the commonly used user-item interactions are transformed into user-item-aspect interactions, which we refer to as interaction triple, resulting in a more nuanced representation of user preference. To validate the effectiveness of our model, we conduct extensive experiments on three real-world datasets. The experimental results show that our model outperforms state-of-the-art baselines, with a 2.0% improvement in prediction accuracy and a substantial 24.5% enhancement in explanation quality for the TripAdvisor dataset.

KGR: A Kernel-Mapping Based Group Recommender System Using Trust Relations

A massive amount of information explosion over the internet has caused a possible difficulty of information overload. To overcome this, Recommender systems are systematic tools that are rapidly being employed in several domains such as movies, travel, E-commerce, and music. In the existing research, several methods have been proposed for single-user modeling, however, the massive rise of social connections potentially increases the significance of group recommender systems (GRS). A GRS is one that jointly recommends a list of items to a collection of individuals based on their interests. Moreover, the single-user model poses several challenges to recommender systems such as data sparsity, cold start, and long tail problems. On the contrary hand, another hotspot for group-based recommendation is the modeling of user preferences and interests based on the groups to which they belong using effective aggregation strategies. To address such issues, a novel “KGR” group recommender system based on user-trust relations is proposed in this study using kernel mapping techniques. In the proposed model, user-trust networks or relations are exploited to generate trust-based groups of users which is one of the important behavioral and social aspects. More precisely, in KGR the group kernels and group residual matrices are exploited as well as seeking a multi-linear mapping between encoded vectors of group-item interactions and probability density function indicating how groups will rate the items. Moreover, to emphasize the relevance of individual preferences of users in a group to which they belong, a hybrid approach is also suggested in which group kernels and individual user kernels are merged as additive and multiplicative models. Furthermore, the proposed KGR is validated on two different trust-based datasets including Film Trust and CiaoDVD. In addition, KGR outperforms with an RMSE value of 0.3306 and 0.3013 on FilmTrust and CiaoDVD datasets which are lower than the 1.8176 and 1.1092 observed with the original KMR.

Does childhood adversity affect household portfolio decisions? Evidence from the Chinese Great Famine

We employ the 1959–1961 Chinese Great Famine as a quasi-natural experiment to examine the relationship between experiencing adversity in childhood and financial decisions in adulthood. Using data from the 2017 China Household Finance Survey and the intensity of excess deaths during the Great Famine, results from our preferred two-part fractional regression model suggest that, for an additional excess death per thousand people during the Famine, cohorts who were in their infancy and early childhood during the Famine are 0.2–0.3 percentage points less likely to hold risky financial assets than other cohorts. We also find that, conditional on the decision to hold risky assets, those who experienced an additional excess death per thousand people during the Famine in their middle to late childhood hold 1.1 percentage points higher share of risky assets in their household financial portfolios than those who did not experience the Famine. We explore several potential mechanisms and find that financial literacy, risk-taking preferences and locus of control are channels through which childhood adversity in the famine years affects household portfolio decisions. Our findings are robust to a series of sensitivity checks.

Predictive modeling of ambulatory outcomes after spinal cord injury using machine learning.

Retrospective multi-site cohort study. To develop an accurate machine learning predictive model using predictor variables from the acute rehabilitation period to determine ambulatory status in spinal cord injury (SCI) one year post injury. Model SCI System (SCIMS) database between January 2000 and May 2019. Retrospective cohort study using data that were previously collected as part of the SCI Model System (SCIMS) database. A total of 4523 patients were analyzed comparing traditional models (van Middendorp and Hicks) compared to machine learning algorithms including Elastic Net Penalized Logistic Regression (ENPLR), Gradient Boosted Machine (GBM), and Artificial Neural Networks (ANN). Compared with GBM and ANN, ENPLR was determined to be the preferred model based on predictive accuracy metrics, calibration, and variable selection. The primary metric to judge discrimination was the area under the receiver operating characteristic curve (AUC). When compared to the van Middendorp all patients (0.916), ASIA A and D (0.951) and ASIA B and C (0.775) and Hicks all patients (0.89), ASIA A and D (0.934) and ASIA B and C (0.775), ENPLR demonstrated improved AUC for all patients (0.931), ASIA A and D (0.965) ASIA B and C (0.803). Utilizing artificial intelligence and machine learning methods are feasible for accurately classifying outcomes in SCI and may provide improved sensitivity in identifying which individuals are less likely to ambulate and may benefit from augmentative strategies, such as neuromodulation. Future directions should include the use of additional variables to further refine these models.