Low-cost Model Research Articles

The effects of free prescriptions on community pharmacy selection: A discrete choice experiment

BackgroundLittle is known about preferences for community pharmacies, particularly the influence of prescription co-payments, and for New Zealand's priority populations, Māori and Pacific Peoples. Improving understanding of community pharmacy preferences will enable tailoring services to meet community needs. ObjectivesThis study aims to investigate New Zealanders’ preferences for community pharmacies when collecting prescriptions. Additionally, variations in preferences for community pharmacy attributes between different latent and demographic groups were examined. MethodsFocus group discussions with various community groups were thematically analyzed to develop six attributes: location, wait time, customer service, prescription co-payments, parking availability, and nearby businesses. Participants were asked to complete an online survey involving 12 choice tasks, where they had to choose their preferred option of 3 unlabeled pharmacies along with demographic questions. A mixed multinomial logit model and latent classes analysis were used to assess and compare the participant preferences. ResultsThe sample of 553 participants, representative of the New Zealand population, resulted in 19,908 observations for analysis. The most attractive pharmacy attribute was its proximity, being within a 10-min travel distance from home or work. The importance of prescription co-payments is evident, with free prescriptions being the second most attractive attribute level and $15 NZD prescription co-payments being the least appealing. Different classes placed importance on different attributes, the largest of which prioritized prescription co-payments. Including demographic characteristics did not improve model accuracy nor predict class membership. ConclusionsUnder current policy, the most effective way for pharmacies to attract business is by offering free prescriptions. However, the trend of adopting lower-cost models may reduce the quality of care they deliver. Policy decision-makers must decide if they are comfortable with this potential impact.

Mechanisms and elemental partitioning during simultaneous dephosphorization and reduction of Fe-O-P melts by hydrogen plasma

The major obstacle to render steelmaking more sustainable is undoubtedly the decarbonization of its process chains. Accompanied by this challenge, the scarcity of high-grade iron ores to be exploited as feedstock is a near reality. This creates a massive dilemma that will force steelmakers to produce green steel from low-grade iron ores. The hydrogen plasma smelting reduction of iron ores (HPSR) emerges as an attractive CO2-lean pathway to produce iron, where the ore is exposed to a reducing hydrogen-containing plasma (Ar-10 % H2) to get simultaneously melted and reduced. Here, we investigate the reduction of low-quality and low-cost model hematite ore samples containing 0.79 wt.% P via HPSR using an electric arc furnace (EAF). The dephosphorization mechanism of the ore was monitored by inspecting the evolving microstructure of the fast-solidified samples, condensed gas on the surface of electrostatic filters installed inside the EAF, as well as quantifying the corresponding P concentration in all constituents (i.e., unreduced oxides, iron and gas). We found that most of the P evaporates already in the beginning of the process (2 min) where the global O concentration in the melt varies from ∼29 to 21 wt.%. The remaining quantities of P, together with other minor gangue-related impurities (especially Si), allocates within the interdendritic domains of the partially reduced samples, as revealed by high-resolution scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX) and atom probe tomography (APT). Complementary thermodynamic modelling underpins the experimental observations, revealing that the evaporation of P is a thermodynamically-driven process, and it is dependent on the oxygen content of the melt. About 97 % dephosphorization was achieved, and only residual amounts of P were found within metallic iron domains, as revealed by APT and chemical analysis of the bulk iron (0.07 wt.% P). Our work aims at providing novel scientific-based perspectives in iron and steelmaking, permitting the production of clean and sustainable iron without the need for secondary metallurgical steps to remove undesired impurities.

Machine Learning Force Field-Aided Cluster Expansion Approach to Phase Diagram of Alloyed Materials.

First-principles approaches based on density functional theory (DFT) have played important roles in the theoretical study of multicomponent alloyed materials. Considering the highly demanding computational cost of direct DFT-based sampling of the configurational space, it is crucial to build efficient and low-cost surrogate Hamiltonian models with DFT accuracy for efficient simulation of alloyed systems with configurational disorder. Recently, the machine learning force field (MLFF) method has been proposed to tackle complicated multicomponent disordered systems. However, the importance of integrating significant physical considerations, including, in particular, convex hull preservation, which is the prerequisite for the accurate prediction of phase diagrams, into the training process of the MLFF remains rarely addressed. In this work, a workflow is proposed to train a convex-hull-preserved (CHP) MLFF for binary alloy systems, based on which the order-disorder phase boundary is predicted by using the Wang-Landau Monte Carlo (WLMC) technique. The predicted values for order-disorder phase transition temperatures agree well with the experiment. The CHP-MLFF is further used to build CE models with the same accuracy as the MLFF and higher efficiency in sampling configurational space. Using the results obtained from the MLFF-based WLMC simulation as a reference, the performances of different schemes for constructing CE models were evaluated in a transparent manner, which revealed the close correlation between the prediction accuracy of ground-state configurations and that of the order-disorder phase transition temperature. This work clearly indicates the great importance of reproducing the convex hull and energetics of ground-state configurations when constructing surrogate Hamiltonians for the statistical modeling of alloyed systems.

Leveraging the Integration of IoT Devices for Energy Optimization in Office Blocks

The integration of Internet of Things (IoT) devices in office buildings has emerged as a promising solution for enhancing energy efficiency and optimizing resource utilization. In the context of modern workplaces, the adoption of smart technologies offers a transformative approach towards achieving energy optimization goals. Internet connected smart devices are ubiquitous and hence opens new opportunities for optimising energy consumption within office blocks. This research paper explores the integration of IoT devices within an office environment to improve energy efficiency and sustainability. The research paper aims to review a simple and low-cost smart workplace model that employs IoT technology, cloud storage, and data analytics for real-time monitoring and analysis of energy consumption patterns within an office environment. The study examines the implementation of an integration of IoT sensors within an office environment enabling HVAC and lighting systems to dynamically adjust energy use based on real-time conditions such as occupancy levels, ambient temperature, and daylight availability. Through the identification of trends, anomalies, and opportunities for optimization, the study seeks to qualify adaptive control strategies that can effectually reduce energy waste and improve general operational productivity in office buildings. The findings from this research underwrite to the growing field of smart offices by demonstrating the potential of integrating IoT devices for energy optimization in modern office blocks.

Many-Body Function Corrected Neural Network with Atomic Attention (MBNN-att) for Molecular Property Prediction.

Recent years have seen a surge of machine learning (ML) in chemistry for predicting chemical properties, but a low-cost, general-purpose, and high-performance model, desirable to be accessible on central processing unit (CPU) devices, remains not available. For this purpose, here we introduce an atomic attention mechanism into many-body function corrected neural network (MBNN), namely, MBNN-att ML model, to predict both the extensive and intensive properties of molecules and materials. The MBNN-att uses explicit function descriptors as the inputs for the atom-based feed-forward neural network (NN). The output of the NN is designed to be a vector to implement the multihead self-attention mechanism. This vector is split into two parts: the atomic attention weight part and the many-body-function part. The final property is obtained by summing the products of each atomic attention weight and the corresponding many-body function. We show that MBNN-att performs well on all QM9 properties, i.e., errors on all properties, below chemical accuracy, and, in particular, achieves the top performance for the energy-related extensive properties. By systematically comparing with other explicit-function-type descriptor ML models and the graph representation ML models, we demonstrate that the many-body-function framework and atomic attention mechanism are key ingredients for the high performance and the good transferability of MBNN-att in molecular property prediction.

Usefulness of low-cost simulation models to learning surgical techniques for placenta accreta spectrum: An observational educational study.

To evaluate the utility of low-cost simulation models to teach surgical techniques for placenta accreta spectrum (PAS), included in a multimodal education workshop for PAS. This was an observational, survey-based study. Participants were surveyed before and after the use of low-fidelity mannequins to simulate two surgical techniques for PAS (one-step conservative surgery [OSCS] and modified subtotal hysterectomy [MSTH]), within a multimodal educational workshop. The workshops included pre-course preparation, didactics, simulated practice of the techniques using low-cost models, and viewing live surgery. Six OSCS/MSTH training workshops occurred across six countries and a total of 270 participants were surveyed. The responses of 127 certified obstetricians and gynecologists (OB-GYNs) were analyzed. Participants expressed favorable impressions of all components of the simulated session. Perceived anatomical simulator fidelity, scenario realism, educational component effectiveness, and self-assessed performance improvement received ratings of 4-5 (positive end of the Likert scale) from over 90% of respondents. When asked about simulation's role in technique comprehension, comfort level in technique performance, and likelihood of recommending this workshop to others, more than 75% of participants rated these aspects with a score of 4-5 (positively) on the five-point scale. Low-cost simulation, within a multimodal education strategy, is a well-accepted intervention for teaching surgical techniques for PAS.

Toward just and equitable mobility: Socioeconomic and perceptual barriers for electric vehicles and charging infrastructure in the United States

Based on a large-scale public survey, we identify and quantify the significance of key factors associated with the deployment of electric vehicles and charging infrastructure. Our results indicate that individual characteristics, such as income, age, region, and single vs. multi-family housing type can significantly affect electric vehicle purchase preferences, especially those concerning overnight charging and perceptions of benefits and barriers. Moreover, our results challenge earlier findings in the literature by showing how certain elements, such as expected electric driving range, certain travel behaviors (e.g., driving distance, destination types), the most common perceived benefits (e.g., cleaner air) or barriers (e.g., reliability concerns), and preferred location for public charging seem to not vary much or at all with the socioeconomic, demographic, and geographical variables examined in this study. We conclude with the implications for policies to advance equitable vehicle electrification. Our findings underscore the importance of lower-cost models of electric vehicles, home and public charging access, charging infrastructure planning, more integrated analysis of interlinked housing and transportation needs and solutions, the availability of alternative transportation modes, and the potential role of gas stations for electric vehicles. We encourage others to build on these results and have shared our complete survey instrument as an added contribution.

1172 Innovative Low-Cost Vascular Anastomosis Simulation Training Model for Vascular Anastomosis Training

Abstract Aim Vascular anastomosis is an important but complex skill requiring mastery by trainees across multiple specialities, with significant risk of suboptimal outcomes. Simulation allows multiple repetitions of a procedure within controlled environment, allowing training without accompanying patient risk. Current simulation courses on vascular anastomosis are expensive and inaccessible to several junior trainees. A low-cost scaffold was developed for vascular anastomosis training and aimed to assess trainee feedback on utility and acceptability. Method The kit was built using following: Six 27 x 0.9 cm latex balloons, Ferrero Rocher chocolate box, Polypropylene 6.0 surgical sutures, Castroviejo needle holder and Forceps. Sharp scissors were used to make openings in the chocolate box, the balloons were then passed through. The kit’s utility was assessed via trainee feedback (N=16, ranked 1-5 where 1 was Unsatisfactory and 5 was Excellent). Results Trainees were able to perform various types of anastomoses: end-to-end, end-to-side, and patching, using our model. The resistance to needle passage through latex balloons was found to closely resemble that of real arteries and veins. Median rating of different categories in trainee feedback were as followed: Improved understanding 4, Helped practice 5, Realistic 4, Appropriateness for learning 4 and Affordability 5, ranging between 3 and 5. Conclusions Improved skills lead to better outcomes in surgical practice. Steps should be taken to make simulation more accessible. Low-cost models help reduce financial impact on trainees and are suitable for a wide range of institutions, including those with limited resources. Subsequent research should assess the effectiveness of this model across various contexts.

Development and Evaluation of a mHealth app - (ReMiT-MS app) for Rehabilitation of Individuals with Relapsing-remitting Multiple Sclerosis - A Mixed Methods, Pragmatic Randomized Controlled Trial - Study Protocol

Background: Delaying or slowing functional loss is a valuable goal of Multiple Sclerosis (MS) rehabilitation. The mHealth app-based exercise rehabilitation intervention is expected to overcome barriers related to routine care of MS. Due to the ubiquitous presence of smartphones, they offer an excellent opportunity for remote monitoring, scheduled interaction with experts, and instruction for exercise in a home environment. Challenges in MS routine care include forgotten rehabilitation steps, limited access to local MS experts, and internal barriers such as low health literacy, mobility limitations, and fatigue, alongside external obstacles like service availability and transport costs. Objectives: To develop a mHealth app that is user-centered and context-specific for rehabilitation of MS symptoms, and to evaluate its clinical and cost effectiveness in individuals with RRMS. Methods: The proposed research will be conducted in two phases; the first phase (Phase 1a) will be focused on the development of mHealth app content (ReMiT-MS app trial I). The pilot phase (Phase 1b), where a prototype of the application will be designed, and its usability will be evaluated. Finally, in the second phase (Phase 2), the clinical and cost-effectiveness of the ReMiT-MS app for the rehabilitation of individuals with RRMS will be evaluated (ReMiT-MS app trial II). Conclusion: The findings of this proposed trial may provide a telerehabilitation platform for individuals with RRMS in a resource-limited setting and establish a low-cost healthcare delivery model. In addition, the results of this research work might open a new window in healthcare delivery in India and similar settings. Trial registration: CTRI/2022/09/045266 [Registered on 06/09/2022]

681 Application of Low-Cost Automated Progress Tracking System (APS) on Laparoscopic Appendicectomy Simulation Models (LaApSi) for the Laparoscopic Simulation Training in the Lower-Middle-Income-Countries (LMICs)

Abstract Aim To assess the effectiveness of the Low-Cost Automated Progress Tracking System on Laparoscopic Appendicectomy (LA) Simulation Models (LaApSi) for Laparoscopic Training in Lower-Middle-Income-Countries (LMICs). To assess and record the progression of trainees’ performance during simulation training. Method After the successful application and validation of the LaApSi model during laparoscopic courses at LMICs, the APS system has been prototyped to assess the trainee’s progression of LA. This system helps to assess surgical steps during simulated LA with the help of the LaApSi model integrated with wires at various anatomical positions giving real-time computer feedback and allowing users to assess the timing of operation, blood vessel excision and injury to the base of the appendix/caecum. Results The prototype version tested in LMIC showed excellent results in the detection of the above-mentioned parameters (shown in photos). This cost-effective model (LaApSi< £5 and docking system <£15) showed huge potential as a laparoscopic training model in LMICs. There will be additional validation of 16 APS models during the Laparoscopic Surgery Course in Nepal in Jan 2024 in collaboration with Nepalese Doctors Association-UK which we believe will further solidify its application in the LMICs Conclusions There is a need for the transition to high-fidelity low-cost laparoscopic simulation models from the traditional training methods in the LMICs which in the long-term address global surgery values. These systems have the potential for remote proctorship in the rural parts adapting the Hub & Spoke model and integrating AI and Machine Learning which eliminates the use of wires and sensors in the models.

Short Caco-2 model for evaluation of drug permeability: A sodium valerate-assisted approach

The human colorectal adenocarcinoma cell line Caco-2, widely used for studying intestinal drug permeability, is typically grown on permeable filter supports and matures in 21 days with frequent media changes. The process is labor-intensive, prone to contamination, and has low throughput, contributing to the overall high utilization cost. Efforts to establish a low-cost, high-throughput, and short-duration model have encountered obstacles, such as weaker tight junctions causing monolayer leaks, incomplete differentiation resulting in low transporter expression, intricate and challenging protocols, and cytotoxicity, limiting the usability. Hence, this study aimed to develop a low-cost, efficient, and short-duration model by addressing the aforementioned concerns by customizing the media and finding a safe differentiation inducer. We generated a new rapid model using sodium valerate, which demonstrated sufficient transporter activity, improved monolayer integrity, and higher levels of differentiation markers than the 21-day model. Furthermore, this model exhibited consistent and reliable results when used to evaluate drug permeability over multiple days of repeated use. This study demonstrates the potential of a sodium valerate-assisted abbreviated model for drug permeability assessment with economic and practical advantages.

BT10 (P025) Community lesion imaging clinics for tele-triage of suspected skin cancer: implementation of a low-cost model in an NHS trust

BT10 (P025) Community lesion imaging clinics for tele-triage of suspected skin cancer: implementation of a low-cost model in an NHS trust

Monitoring the power flow of a low voltage distribution network using public lighting infrastructure

The production of renewable energy sources can change rapidly in a short period of time, making monitoring the power quality of Low Voltage Distribution Networks (LVDNs) one of the biggest challenges for Distribution System Operators (DSOs). An LVDN can overload some equipment if it does not monitors voltage profiles, voltage unbalance and reverse apparent power flow. Therefore, we propose a monitoring system that uses the Public Lighting Infrastructure (PLI), which is fed directly by the LVDN, to monitor the LVDN. It is based on remote management relays installed in the PLI, and replaces the installation of a power quality analyzer (PQ). The system has been empirically validated and the results show that the proposed system can identify reverse apparent power flow, profiles, and voltage unbalance with an average absolute error of 0.096% compared to a professional PQ analyzer. The proposed system can be a relevant contribution to the challenge of power quality monitoring, with cost savings; for example, DSOs can use the proposed system to access real-time information in the LVDN through a low-cost data acquisition model, without the need to send a team to install a PQ analyzer in the LVDN and then obtain the data.

Building a Low-Cost Model for Predicting Infectious Disease Burden: Dengue in Brazil

Disease surveillance is an essential function of public health ministries, but the process is expensive and slow. Here, I consider the potential of a new low-cost early warning method that policy makers could use to immediately estimate the incidence of Dengue in Brazil. Using statewide data on the frequency of “Dengue” in Google search trends, lagged epidemiological data, climate data, and state and time fixed effects, I predict values for Dengue for each month in each state. Comparing predicted values to observed Dengue incidence, this model explained 72.2% of the variation in observed Dengue but tended to underestimate Dengue incidence. Accuracy could be greatly improved using more localized data with smaller time scales. This technique uses free and instantly available data, is quite accurate, and can be modified for other diseases and geographic contexts. Google trends data, when combined with publicly available metrics, provides a viable and productive alternative or complement to current epidemiological surveillance infrastructure and could expedite public health response at little to no cost to national health ministries.

Low-cost cold porcelain anatomical models: An affordable complementary teaching tool.

Human cadavers are an important component of anatomy education; however, they have limitations. In this scenario, alternative artificial models emerge to complement teaching and enable learning in environments with scarce resources. The following study aims to demonstrate the elaboration of handmade cold porcelain models and their acceptance by medical students in a Brazilian university. A cross-sectional study was carried out with 70 first-year medical students using quantitative and qualitative analysis. The students' evaluation was analysed through an online questionnaire with a Likert scale and an open-ended question. The present study was approved by the local Research Ethics Committee. Five models were produced with cold porcelain and resin. Qualitative evaluation highlighted the ease and practicality of the models. Students agree with the idea that the use of cadavers and handmade anatomical pieces contribute to their learning, that the handmade models are very similar to the human body, and they strongly agree that the use of both made it easier to understand the content. However, they also see the models as complementary materials, disagreeing with the idea that they are the best or the only ways to teach. In the word cloud, the words "unreal" and "complement" are often highlighted converging to the idea that was also observed in the quantitative analysis that handmade models are a complementary teaching tool. Cold porcelain handmade models enable the democratisation of anatomy teaching; hence, they are low-cost, easy to access, and allow the reproducibility of anatomical structures by each student. Anatomical models that emerge, such as handmade models, are an important complement to anatomy education and a solution for places with scarce resources.

Construction scheduling optimization in the prefabricated buildings through multi-objective GA on the principle of sustainable development

To save resources and protect the environment to the maximum extent, green buildings came into being. Among them, prefabricated building is the only way for traditional buildings to transform into green buildings. The construction scheduling of traditional buildings is mostly focused on the control of on-site resources, which cannot scientifically and reasonably complete the construction goal of prefabricated building. In response to the above issues, a resource constrained scheduling model based on genetic algorithm is designed by sustainable development, and an improved non dominated sorting genetic algorithm with elite strategy is introduced. It is used to solve the time cost weight balance scheduling model and the low-cost low-carbon scheduling model. The research results indicated that this algorithm had a reverse generation distance value of 0.35 when evaluated 4000 times, and a super volume value of 0.43 when evaluated 10000 times. In the application of a certain affordable housing project, the resource constrained scheduling model based on genetic algorithm can shorten the assembly phase to 8 days, and the low-cost low-carbon scheduling model using proposed algorithm can reduce the transportation cost and carbon emission duration of transportation vehicles to 22501 yuan and 93.75 h, respectively. Resource constrained scheduling models based on genetic algorithms and low-cost low-carbon scheduling models have potential in the field of green buildings, which can achieve significant results in saving time, cost, and reducing carbon emissions. These research results can provide reference for the promotion and practice of green buildings, and guide the formulation and implementation of relevant policies.

A Machine Learning Approach for Prediction of CDAI Remission with TNF Inhibitors: A Concept of Precision Medicine from the FIRST Registry.

This study aimed to develop low-cost models using machine learning approaches predicting the achievement of Clinical Disease Activity Index (CDAI) remission 6months after initiation of tumor necrosis factor inhibitors (TNFi) as primary biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) for rheumatoid arthritis (RA). Data of patients with RA initiating TNFi as first b/tsDMARD after unsuccessful methotrexate treatment were collected from the FIRST registry (August 2003 to October 2022). Baseline characteristics and 6-month CDAI were collected. The analysis used various machine learning approaches including logistic regression with stepwise variable selection, decision tree, support vector machine, and lasso logistic regression (Lasso), with 48 factors accessible in routine clinical practice for the prediction model. Robustness was ensured by k-fold cross validation. Among the approaches tested, Lasso showed the advantages in predicting CDAI remission: with a mean area under the curve 0.704, sensitivity 61.7%, and specificity 69.9%. Predicted TNFi responders achieved CDAI remission at an average rate of 53.2%, while only 26.4% of predicted TNFi non-responders achieved remission. Encouragingly, the models generated relied solely on patient-reported outcomes and quantitative parameters, excluding subjective physician input. While external cohort validation is warranted for broader applicability, this study highlights the potential for a low-cost predictive model to predict CDAI remission following TNFi treatment. The approach of the study using only baseline data and 6-month CDAI measures, suggests the feasibility of establishing regional cohorts to generate low-cost models tailored to specific regions or institutions. This may facilitate the application of regional/in-house precision medicine strategies in RA management.

Internal validation of Automated Visual Evaluation (AVE) on smartphone images for cervical cancer screening in a prospective study in Zambia.

Visual inspection with acetic acid (VIA) is a low-cost approach for cervical cancer screening used in most low- and middle-income countries (LMICs) but, similar to other visual tests, is subjective and requires sustained training and quality assurance. We developed, trained, and validated an artificial-intelligence-based "Automated Visual Evaluation" (AVE) tool that can be adapted to run on smartphones to assess smartphone-captured images of the cervix and identify precancerous lesions, helping augment VIA performance. Prospective study. Eight public health facilities in Zambia. A total of 8204 women aged 25-55. Cervical images captured on commonly used low-cost smartphone models were matched with key clinical information including human immunodeficiency virus (HIV) and human papillomavirus (HPV) status, plus histopathology analysis (where applicable), to develop and train an AVE algorithm and evaluate its performance for use as a primary screen and triage test for women who are HPV positive. Area under the receiver operating curve (AUC); sensitivity; specificity. As a general population screening tool for cervical precancerous lesions, AVE identified cases of cervical precancerous and cancerous (CIN2+) lesions with high performance (AUC = 0.91, 95% confidence interval [CI] = 0.89-0.93), which translates to a sensitivity of 85% (95% CI = 81%-90%) and specificity of 86% (95% CI = 84%-88%) based on maximizing the Youden's index. This represents a considerable improvement over naked eye VIA, which as per a meta-analysis by the World Health Organization (WHO) has a sensitivity of 66% and specificity of 87%. For women living with HIV, the AUC of AVE was 0.91 (95% CI = 0.88-0.93), and among those testing positive for high-risk HPV types, the AUC was 0.87 (95% CI = 0.83-0.91). These results demonstrate the feasibility of utilizing AVE on images captured using a commonly available smartphone by nurses in a screening program, and support our ongoing efforts for moving to more broadly evaluate AVE for its clinical sensitivity, specificity, feasibility, and acceptability across a wider range of settings. Limitations of this study include potential inflation of performance estimates due to verification bias (as biopsies were only obtained from participants with visible aceto-white cervical lesions) and due to this being an internal validation (the test data, while independent from that used to develop the algorithm was drawn from the same study).

Racial and Ethnic Disparities in Barriers to Mental Health Treatment Among U.S. College Students.

Using a sample of U.S. college students, the authors evaluated whether barriers to mental health treatment varied by race and ethnicity. Data were drawn from a large multicampus study conducted across 26 U.S. colleges and universities. The sample (N=5,841) included students who screened positive for at least one mental disorder and who were not currently receiving psychotherapy. The most prevalent barriers to treatment across the sample were a preference to deal with issues on one's own, lack of time, and financial difficulties. Black and Hispanic/Latine students reported a greater willingness to seek treatment than did White students. However, Black and Hispanic/Latine students faced more financial barriers to treatment, and Hispanic/Latine students also reported lower perceived importance of mental health. Asian American students also reported financial barriers and preferred to handle their issues on their own or with support from family or friends and had lower readiness, willingness, and intentionality to seek help than did White students. Disparities in unmet treatment needs may arise from both distinct and common barriers and point to the potential benefits of tailored interventions to address the specific needs of students of color from various racial and ethnic backgrounds. The findings further underscore the pressing need for low-cost and brief treatment models that can be used or accessed independently to address the most prevalent barriers for students.

Low-Cost 3D Models for Cervical Spine Tumor Removal Training for Neurosurgery Residents.

Spinal surgery, particularly for cervical pathologies such as myelopathy and radiculopathy, requires a blend of theoretical knowledge and practical skill. The complexity of these conditions, often necessitating surgical intervention, underscores the need for intricate understanding and precision in execution. Advancements in neurosurgical training, especially with the use of low-cost 3D models for simulating cervical spine tumor removal, are revolutionizing this field. These models provide the realistic and hands-on experience crucial for mastering complex neurosurgical techniques, filling gaps left by traditional educational methods. This study aimed to assess the effectiveness of 3D-printed cervical vertebrae models in enhancing surgical skills, focusing on tumor removal, and involving 20 young neurosurgery residents. These models, featuring silicone materials to simulate the spinal cord and tumor tissues, provided a realistic training experience. The training protocol included a laminectomy, dural incision, and tumor resection, using a range of microsurgical tools, focusing on steps usually performed by senior surgeons. The training program received high satisfaction rates, with 85% of participants extremely satisfied and 15% satisfied. The 3D models were deemed very realistic by 85% of participants, effectively replicating real-life scenarios. A total of 80% found that the simulated pathologies were varied and accurate, and 90% appreciated the models' accurate tactile feedback. The training was extremely useful for 85% of the participants in developing surgical skills, with significant post-training confidence boosts and a strong willingness to recommend the program to peers. Continuing laboratory training for residents is crucial. Our model offers essential, accessible training for all hospitals, regardless of their resources, promising improved surgical quality and patient outcomes across various pathologies.