Evaluation Plan Research Articles

Treatment planning evaluation and experimental validation of the magnetic resonance-based intrafraction drift correction

Background and purposeMRI-guided online adaptive treatments can account for interfractional variations, however intrafraction motion reduces treatment accuracy. Intrafraction plan adaptation methods, such as the Intrafraction Drift Correction (IDC) or sub-fractionation, are needed. IDC uses real-time automatic monitoring of the tumor position to initiate plan adaptations by repositioning segments. IDC is a fast adaptation method that occurs only when necessary and this method could enable margin reduction. This research provides a treatment planning evaluation and experimental validation of the IDC. Materials and methodsAn in silico treatment planning evaluation was performed for 13 prostate patients mid-treatment without and with intrafraction plan adaptation (IDC and sub-fractionation). The adaptation methods were evaluated using dose volume histogram (DVH) metrics. To experimentally verify IDC a treatment was mimicked whereby a motion phantom containing an EBT3 film moved mid-treatment, followed by repositioning of segments. In addition, the delivered treatment was irradiated on a diode array phantom for plan quality assurance purposes. ResultsThe planning study showed benefits for using intrafraction adaptation methods relative to no adaptation, where the IDC and sub-fractionation showed consistently improved target coverage with median target coverages of 100.0%. The experimental results verified the IDC with high minimum gamma passing rates of 99.1% and small mean dose deviations of maximum 0.3%. ConclusionThe straightforward and fast IDC technique showed DVH metrics consistent with the sub-fractionation method using segment weight re-optimization for prostate patients. The dosimetric and geometric accuracy was shown for a full IDC workflow using film and diode array dosimetry.

Looking back, looking forward: lessons from COVID-19 communication measurement, evaluation and learning (MEL).

Communication is an integral component of an emergency response, including to the coronavirus disease (COVID-19) pandemic. Designing effective communication requires systematic measurement, evaluation and learning. In the Western Pacific Region, the World Health Organization (WHO) responded to the COVID-19 pandemic by using the Communication for Health (C4H) approach. This included the development and application of a robust measurement, evaluation and learning (MEL) framework to assess the effectiveness of COVID-19 communication, and to share and apply lessons in real time to continuously strengthen the pandemic response. MEL was applied during the planning, implementation and summative evaluation phases of COVID-19 communication, with evidence-based insights and recommendations continuously integrated in succeeding phases of the COVID-19 response. This article captures good practices that helped WHO to implement MEL during the COVID-19 pandemic. It focuses on lessons from the evaluation process, including the importance of planning, data integration, collaboration, partnerships, piggybacking, using existing data and leveraging digital media. Despite some limitations, the systematic application of MEL to COVID-19 communication shows its value in the planning and implementation of effective, evidence-based communication to address public health challenges. It enables the evaluation of outcomes and reflection on lessons identified to strengthen the response to the current pandemic and future emergencies.

Improving students' literacy and numeracy skills at Sekolah Dasar Negeri 1 Liabuku, Baubau City

This community service activity is based on the low numeracy and literacy skills of students at SD Negeri 1 Liabuku, Baubau City. The aim of this service is to explore and enhance students' literacy and numeracy skills. The participants in this PKM activity were students from SD Negeri 1 Liabuku, Baubau City. This service activity takes the form of tutoring, which aims to improve and familiarize children with literacy and numeracy. The activity is implemented in three stages: the planning stage, implementation stage, and evaluation stage. The result of this service is a comprehensive and structured numeracy and literacy program at SD Negeri 1 Liabuku, Baubau City. The implementation of the program is aligned with three stages: habituation, development, and learning. Using a learning-through-play model and learning media, students' numeracy and literacy skills both inside and outside the classroom can significantly influence the development of their respective talents and skills.

Emotional Experiences of Preschool Teachers During Learning Evaluation: A Phenomenological Study

This study explores the emotional experiences of preschool teachers during the implementation of learning evaluations. Utilizing a qualitative phenomenological approach, the research captures the lived experiences of 18 preschool teachers in Banten, Indonesia. Data were collected through in-depth interviews and observations from April to December 2023, employing snowball sampling to ensure relevant expertise. Thematic analysis revealed that teachers predominantly experienced positive emotions, such as pride, joy, and gratitude, during the planning and preparation of evaluation instruments. However, negative emotions, including anxiety and discomfort, emerged during the implementation and follow-up stages. These negative emotions were often linked to concerns about meeting educational targets and managing classroom dynamics effectively. The study identifies that positive emotions were associated with the perceived importance of evaluation planning and successful execution, while negative emotions were connected to fears of underperformance and external pressures. These findings highlight preschool teachers' complex emotional landscape, emphasizing the need for professional development programs focusing on emotional regulation and stress management. The study's implications suggest that fostering a supportive school environment can enhance teachers' well-being and effectiveness, ultimately benefiting student outcomes. Limitations include the small, geographically limited sample and reliance on self-reported data, which may introduce bias. Future research should expand the sample size, incorporate diverse geographical locations, and utilize mixed-methods approaches to comprehensively understand teachers' emotional experiences. Such research would enable the development of more effective support strategies for educators, contributing to improved educational practices and teacher satisfaction.

Examination of the selection of logistics service providers

The selection of logistics service providers plays a crucial role in the success of an organization's supply chain management. The new industrial revolution taking place today provides solutions that prioritize the issue of quality and also raise reliability to a new level, both on the service provider's and user's sides. As businesses grow and expand, the need for efficient and reliable logistics services becomes increasingly important. This study examines how to determine the criteria for the selection of logistics service providers in the current technological environment. The choice of logistics service provider has significant implications for an organization's operations and overall performance. Optimum selection can lead to enhanced customer satisfaction, cost savings, improved efficiency, and a competitive advantage. However, poor selection can result in logistical inefficiencies, decreased customer satisfaction, increased costs, and negative impacts on organizational reputation. Hence, careful consideration and evaluation of potential service providers are crucial. The digitized environment offers a solution for accessing large-scale databases, which provide well-founded decision evaluation plans based on a large number of samples. The quality characteristics influencing the logistics parameters were examined and weighted from the perspective of customer requirements. By exploring various aspects, we aim to shed light on the intricacies of this process and provide insights that can assist organizations in making informed decisions. We attempt to make the indices that appear as bottlenecks in the specified order more efficient using an optimization procedure.

Improved identification of fracture total factor characteristics and cross-scale flow channels by interpreting initial production data of horizontal wells

Fracture identification after hydraulic fracturing of tight gas reservoirs is crucial to fracturing evaluation, productivity analysis, and production plan. Owing to fracture branching, bending, extending, and reversing in reservoirs, a simplified straight fracture cannot fully describe fracture characteristics. The novelty of this study is that the coupling mechanism between the complex fracture network characteristics and well production performances is figured out, thus achieving the identification of fracture morphology in fractured reservoirs. In this study, a combined model including Laplace transform, boundary element method, Stehfest numerical inversion, and fractal theory presents transient well performances. The key contributions to productivities at different production stages are dynamically identified via a sensitivity analysis. The results clearly indicate that the fracture total length is the key factor determining productivities due to no fracture interference at initial production stage. Furthermore, the logarithm of productivity linearly depends on the logarithm of production time and the productivity contribution rate equals the ratio of fracture length to total length. Thus, an updated inversion method is proposed by interpreting initial production data based on a tree-shaped fracture network associated with fracture total factor and cross-scale characteristics. The inversion results well match with their targets, with an error of no more than 5 %, ensuring the accuracy and validity of the proposed method. This innovative approach with low requirements on field data is convenient to yield an accurate prediction of fracture morphology, thereby providing a feasible strategy for promoting sustainable production of clean energy in unconventional gas reservoirs.

The impact of digital imaging and computer-assisted surgery in oral surgery: A narrative review

Digital imaging and computer-assisted surgery (CASS) have ushered in a new era in oral surgery, offering manifold advantages in terms of precision, efficiency, and patient well-being. This narrative review delves into the profound impact of these technologies across various facets of oral surgery, encompassing diagnostics, treatment planning, intraoperative guidance, and postoperative evaluation. We examine the evolution of digital imaging modalities, notably cone beam computed tomography (CBCT), and their seamless integration with computer-assisted tools for meticulous surgical interventions. Furthermore, we explore the benefits conferred by virtual surgical planning (VSP) and 3D printing in refining preoperative strategizing and surgical execution. Additionally, we underscore the pivotal role played by navigation systems and robotic assistance in heightening surgical accuracy and mitigating complications. Notwithstanding the remarkable advantages, we also address the inherent challenges and limitations associated with digital imaging and CASS, including cost considerations, learning curves, and potential errors. Despite these hurdles, the widespread adoption of digital technologies in oral surgery has substantially revolutionized clinical practices, culminating in more predictable outcomes and heightened patient satisfaction. Lastly, we contemplate future avenues for research aimed at optimizing the integration of digital imaging and computer-assisted techniques in oral surgical procedures.

러-우 전쟁사례 분석을 통한 드론 대테러 함정 생존성 설계 및시험평가 발전방안

Since the positive viability field remains the way it was in the 2010s, the ship test evaluation in the viability field is being applied to withstand the attack on an extremely limited kind of weapon system even the latest ship. In response, this paper analyzed and considered maritime warfare on anti-drone for counter-terrorism during the Russia-Ukraine War, which is an example of the latest war paradigm shift, and diagnosed the status of test evaluation in the field of Naval ship survivability to derive realistic development measures such as reflecting threat development trends, strengthening attack and damage control evaluation, confirming ship viability limits, and realizing test evaluation plans.

Utility and Implementation of the Distress Thermometer for Cancer Patients: A Cross-Sectional Study From Saudi Arabia.

Background Cancer patients suffer from variable degrees of distress. The distress thermometer (DT) is a valuable tool for screening those patients for distress. Few studies have addressed the utility of DT in screening cancer patients in Saudi Arabia. We aimed to measure the distress level of adult cancer patients utilizing the DT and identify the appropriate measures and interventions required to improve this population's well-being. Methods This cross-sectional study was carried out at the oncology center of King Saud University Medical City (KSUMC), Riyadh, Saudi Arabia. Enrollment criteria were Saudi adults (≥14 years old), with a diagnosis of cancer, whogave informed consent. They were screened for distress using the DT and its associated problem list (PL). A workflow for a psycho-oncology supportive program was suggested. Results Using DT at a cut-off score of ≥4, 22% of patients had significant distress. The most frequent problems reported were loss/change of physical activity, swelling/edema, change in eating, family health problems, and child care. The multivariable binary regression analysis showed that sadness, depression, worry/anxiety, fear, loss of interest, change in appearance, taking care of myself, swelling/edema, and memory/concentration problems were independent factors for significant distress in our cohort. The suggested workflow could effectively be implemented among cancer patients. Conclusion The current study's findings support previous reports concerning the utility of DT in screening cancer patients for distress. A considerable number of Saudi cancer patients suffered from significant distress, which was significantly related to the emotional, spiritual, social, and religious aspects of their problems. We suggested a workflow by which cancer centers can implement DT screening after developing a plan for timely distress evaluation, with further proper management and referrals accordingly. Additional studies are warranted.

Are rural places prepared for the energy transition? An evaluation of land use plans in rural Manitoba

ABSTRACT As energy systems transition from fossil fuels to renewable sources, energy infrastructure will become a predominant land use, especially in rural areas. This poses a challenge for municipalities who need to adapt land use planning frameworks to more renewable energy. Using plan quality evaluation, this paper assesses how 57 rural official plans in Manitoba (Canada) are attempting to integrate energy planning. Consideration of energy within plans is poor, especially for implementation mechanisms and fact base. While rural communities are the forefront of the energy transition, existing land use planning frameworks are maladapted to enable it.

Probabilistic linguistic prospect outranking risk decision making method based on stochastic dominance and application in emergency plan evaluation

In the decision-making process, probabilistic linguistic term sets (PLTSs) could represent decision-makers’ uncertain evaluation information. The combination of probabilistic and linguistic information assists decision-makers to depict their evaluation information more flexibly. In this paper, a prospect outranking decision-making method based on stochastic dominance is proposed for solving the multi-attribute risk decision-making problem under the probabilistic linguistic information environment. Firstly, to fully preserve the original probabilistic linguistic information, we give the definitions of the generalized probability distribution and the corresponding generalized cumulative distribution function of a PLTS for defining the distance measure between PLTSs. Secondly, based on the positive and negative ideal cumulative distribution functions, we propose the probabilistic linguistic prospect value functions. After that, considering the uncertainty of the relationship between alternatives, several kinds of novel prospect stochastic dominance and almost stochastic dominance relations are defined, which consider the decision makers’ distinction between gains and losses to measure the subtle relationship between alternatives. The stochastic dominance approach and the almost stochastic dominance approach under the probabilistic linguistic decision-making environment are put forward. Besides, we use the superiority and inferiority ranking method to solve the dominance ranking problem of the uncertainty relationship between any two alternatives. Finally, the proposed method is applied in the evaluation of emergency plans to show the accuracy and reliability, and comparative analyses are made to illustrate its availability.

DECIDE: A decoupled semantic and boundary learning network for precise osteosarcoma segmentation by integrating multi-modality MRI

Automated Osteosarcoma Segmentation in Multi-modality MRI (AOSMM) holds clinical significance for effective tumor evaluation and treatment planning. However, the precision of AOSMM is challenged by the diverse characteristics of multi-modality MRI and the inherent heterogeneity and boundary ambiguity of osteosarcoma. While numerous methods have made significant strides in automated osteosarcoma segmentation, they primarily focused on the use of a single MRI modality and overlooked the potential benefits of integrating complementary information from other MRI modalities. Furthermore, they did not adequately model the long-range dependencies of complex tumor features, which may lead to insufficiently discriminative feature representations. To this end, we propose a decoupled semantic and boundary learning network (DECIDE) to achieve precise AOSMM with three functional modules. The Multi-modality Feature Fusion and Recalibration (MFR) module adaptively fuses and recalibrates multi-modality features by exploiting their channel-wise dependencies to compute low-rank attention weights for effectively aggregating useful information from different MRI modalities, which promotes complementary learning between multi-modality MRI and enables a more comprehensive tumor characterization. The Lesion Attention Enhancement (LAE) module employs spatial and channel attention mechanisms to capture global contextual dependencies over local features, significantly enhancing the discriminability and representational capacity of intricate tumor features. The Boundary Context Aggregation (BCA) module further enhances semantic representations by utilizing boundary information for effective context aggregation while also ensuring intra-class consistency in cases of boundary ambiguity. Substantial experiments demonstrate that DECIDE achieves exceptional performance in osteosarcoma segmentation, surpassing state-of-the-art methods in terms of accuracy and stability.

Deep Learning Framework for Liver Tumor Segmentation

INTRODUCTION: Segregating hepatic tumors from the liver in computed tomography (CT) scans is vital in hepatic surgery planning. Extracting liver tumors in CT images is complex due to the low contrast between the malignant and healthy tissues and the hazy boundaries in CT images. Moreover, manually detecting hepatic tumors from CT images is complicated, time-consuming, and needs clinical expertise.
 OBJECTIVES: An automated liver and hepatic malignancies segmentation is essential to improve surgery planning, therapy, and follow-up evaluation. Therefore, this study demonstrates the creation of an intuitive approach for segmenting tumors from the liver in CT scans.
 METHODS: The proposed framework uses residual UNet (ResUNet) architecture and local region-based segmentation. The algorithm begins by segmenting the liver, followed by malignancies within the liver envelope. First, ResUNet trained on labeled CT images predicts the coarse liver pixels. Further, the region-level segmentation helps determine the tumor and improves the overall segmentation map. The model is tested on a public 3D-IRCADb dataset.
 RESULTS: Two metrics, namely dice coefficient and volumetric overlap error (VOE), were used to evaluate the performance of the proposed method. ResUNet model achieved dice of 0.97 and 0.96 in segmenting liver and tumor, respectively. The value of VOE is also reduced to 1.90 and 0.615 for liver and tumor segmentation.
 CONCLUSION: The proposed ResUNet model performs better than existing methods in the literature. Since the proposed model is built using U-Net, the model ensures quality and precise dimensions of the output.

Online Reinforcement Learning-Based Pedagogical Planning for Narrative-Centered Learning Environments

Pedagogical planners can provide adaptive support to students in narrative-centered learning environments by dynamically scaffolding student learning and tailoring problem scenarios. Reinforcement learning (RL) is frequently used for pedagogical planning in narrative-centered learning environments. However, RL-based pedagogical planning raises significant challenges due to the scarcity of data for training RL policies. Most prior work has relied on limited-size datasets and offline RL techniques for policy learning. Unfortunately, offline RL techniques do not support on-demand exploration and evaluation, which can adversely impact the quality of induced policies. To address the limitation of data scarcity and offline RL, we propose INSIGHT, an online RL framework for training data-driven pedagogical policies that optimize student learning in narrative-centered learning environments. The INSIGHT framework consists of three components: a narrative-centered learning environment simulator, a simulated student agent, and an RL-based pedagogical planner agent, which uses a reward metric that is associated with effective student learning processes. The framework enables the generation of synthetic data for on-demand exploration and evaluation of RL-based pedagogical planning. We have implemented INSIGHT with OpenAI Gym for a narrative-centered learning environment testbed with rule-based simulated student agents and a deep Q-learning-based pedagogical planner. Our results show that online deep RL algorithms can induce near-optimal pedagogical policies in the INSIGHT framework, while offline deep RL algorithms only find suboptimal policies even with large amounts of data.

Manajemen Kurikulum Pendidikan Islam di Pondok Pesantren Al-Husna Riau

Curriculum management is a cooperative, comprehensive, systemic and systematic curriculum management system in order to realize the achievement of curriculum objectives. The implementation of curriculum management must develop in accordance with the context of school-based management (MBS) and the education unit level curriculum (KTSP). Success in the implementation of Islamic education institutions will depend heavily on the components of Islamic education curriculum management such as teaching and education staff management, school management, subject management, subject schedule management, financing management, and infrastructure management. These components are an integral part of efforts to achieve the goals of educational institutions, especially Islamic education based on Islamic boarding schools. The method used in this research is a qualitative method with a descriptive approach. The techniques used in collecting data were observation, in-depth interviews and documentation. Data analysis was carried out by categorizing and interpreting data in narrative form. Curriculum management in the national education system is a very complex process and involves various interrelated components in order to direct all forms of educational activities to achieve national education goals. Curriculum management procedures consist of curriculum planning, curriculum implementation, and curriculum evaluation.

Abstract 6974: Spatial exosome analysis reveals the location heterogeneity of exosomal miRNAs in ovarian cancer patients

Abstract Extracellular vesicles (EVs), including exosomes, are recognized as promising functional tools involved in cancer progression. Although evidence of the functions of EV in cancer biology has been accumulating, optimizing EV isolation is still a major challenge. In ovarian cancer, peritoneal dissemination is a crucial prognostic factor due to the lack of effective treatment, and EVs in malignant ascites play a key role for progression. In this study, we invented cellulose nanofiber (CNF) sheets which enable to capture EVs from approximately 10 μL of biofluids and enabled the analysis of the bioactive molecules included in the EVs (Nature Communications 2023). By attaching CNF sheets to moistened organs, the EVs in trace amounts of ascites were collected, which was sufficient to perform small RNA sequence analyses. In the ovarian cancer mouse model, CNF sheets enabled the detection of cancer-associated miRNAs from the very early phase when the mice did not have apparent ascites, and the EVs from different locations, such as pelvic walls or liver surfaces, had unique miRNA profiles. To confirm the utility of the CNF sheets in cancer patients, we directly collected the EVs on the organ surfaces by the EV attachment method during surgery under appropriate ethical institutional approval with individual consent. EVs were collected from the pelvic peritoneum, omentum, liver surface, or tumor surface, and the miRNA profiling demonstrated each location formed unique clusters. Notably, several tumor-suppressive miRNAs have been identified in liver surface EVs, such as miR-122-3p, and this is consistent with actual ovarian cancer cell progression, which tends to metastasize to the diaphragm rather than to the liver surface. In addition, the trajectory analysis revealed that the pattern of connection in each site was different between patients with localized and advanced disease. We identified ten miRNAs were high in tumors and in EVs from tumor rupture sites, and the level of each miRNA decreased with distance from the primary tumors. Among ten miRNAs, hsa-miR-200b-3p and hsa-miR-429 were confirmed as highly expressed in tumor tissues compared to adjacent normal tissues or benign tumors, and three miRNAs in serum, urine and saliva decreased post-surgery. In summary, spatial CNF sheet analyses in patient bodies identified location-based heterogeneity in EV-miRNA profiles, and the profiles reflected disease conditions. This new method could provide unveiled biological aspects of EVs, and suggest a clinical strategy contributing to cancer diagnosis, staging evaluation, and therapy planning. Citation Format: Akira Yokoi, Kosuke Yoshida, Yusuke Yamamoto, Takao Yasui, Hiroaki Kajiyama. Spatial exosome analysis reveals the location heterogeneity of exosomal miRNAs in ovarian cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6974.

Qualitative evaluation of artificial intelligence-generated weight management diet plans.

The transformative potential of artificial intelligence (AI), particularly via large language models, is increasingly being manifested in healthcare. Dietary interventions are foundational to weight management efforts, but whether AI techniques are presently capable of generating clinically applicable diet plans has not been evaluated. Our study sought to evaluate the potential of personalized AI-generated weight-loss diet plans for clinical applications by employing a survey-based assessment conducted by experts in the fields of obesity medicine and clinical nutrition. We utilized ChatGPT (4.0) to create weight-loss diet plans and selected two control diet plans from tertiary medical centers for comparison. Dietitians, physicians, and nurse practitioners specializing in obesity medicine or nutrition were invited to provide feedback on the AI-generated plans. Each plan was assessed blindly based on its effectiveness, balanced-ness, comprehensiveness, flexibility, and applicability. Personalized plans for hypothetical patients with specific health conditions were also evaluated. The primary outcomes measured included the indistinguishability of the AI diet plan from human-created plans, and the potential of personalized AI-generated diet plans for real-world clinical applications. Of 95 participants, 67 completed the survey and were included in the final analysis. No significant differences were found among the three weight-loss diet plans in any evaluation category. Among the 14 experts who believed that they could identify the AI plan, only five did so correctly. In an evaluation involving 57 experts, the AI-generated personalized weight-loss diet plan was assessed, with scores above neutral for all evaluation variables. Several limitations, of the AI-generated plans were highlighted, including conflicting dietary considerations, lack of affordability, and insufficient specificity in recommendations, such as exact portion sizes. These limitations suggest that refining inputs could enhance the quality and applicability of AI-generated diet plans. Despite certain limitations, our study highlights the potential of AI-generated diet plans for clinical applications. AI-generated dietary plans were frequently indistinguishable from diet plans widely used at major tertiary medical centers. Although further refinement and prospective studies are needed, these findings illustrate the potential of AI in advancing personalized weight-centric care.

Identification of the strategic intents in Iranian public schools

what has been felt for years in the field of education planning and management is the evaluation of clear, accurate planning, measurement, strategy and evaluation for the future. In the field of very complex activities that today's organizations determine the influencing factors, the speed of change and transformations, organizations need strategic planning and strategic management and identification of strategic intent in order to achieve long-term success and survival.This article identifies the strategic intents of developing public schools and prioritizing them.The statistical population of current study includes the experts who are aware of the strategic and educational planning, education management and public schools of Iran. Using judgmental sampling, 20 experts were selected as samples.The methodology is a mix of Delphi-survey method. According to the stages of study, using Delphi method, the strategic intents of developing public schools were first identified. Then, through Freidman test, the identified strategic intents were prioritized. The used tools to gather data is researcher made questionnaire. 44 identified strategic intents for developing public schools were categorized in three dimensions of opportunism, growth seeking and futurism. The obtained results of study indicate that 1) prior strategic intents in dimensions of opportunism are “learners’ steps towards their talents”, “attention to the sustainable development” and “providing proper human resources for society”, 2) prior strategic intents in dimension of growth are “moving towards knowledge-based societies”, “the growth of culture, education and research” and “expanding and deepening the culture of research and evaluation” and 3) prior strategic intents in dimension of futurism include “creating the model of developmental education”, “approaching to the global development” and “modern and excellent Islamic civilization”

Shared Success: Developing a Plan to Evaluate the Implementation and Impact of Medicaid Expansion in North Carolina.

The North Carolina Department of Health and Human Services plans to utilize a data-driven approach to evaluate the impacts of Medicaid expansion in the state. The evaluation plan includes tracking enrollment counts, creating metrics for monitoring of activities, setting measurable goals, and involving trusted community partners throughout.

Transition self-assessment tool: The development and field testing of a statewide assessment of pre-employment transition services

BACKGROUND: Under the Workforce Innovation and Opportunity Act of 2014 (WIOA), vocational rehabilitation (VR) agencies are required to evaluate their students’ need for pre-employment transition services and use this information to inform their goals, priorities and strategies. OBJECTIVE: This manuscript presents the development and field testing of the Transition Self-Assessment Tool (TSAT), a web-based instrument designed to support VR agencies’ evaluation and strategic planning initiatives by measuring the availability, accessibility, and coordination of school-based transition services. METHOD: Our methods describe the TSAT development procedures and the methods of conducting a TSAT field test. RESULTS: Findings suggest high schools offer a broad array of career-related services for their students. However, not all potentially eligible students with disabilities have access to those services and when these services are provided, they are frequently delivered without coordination with VR. Additionally, data show more services are available, accessed, and coordinated in high schools participating in a statewide transition program. CONCLUSION: Results from the TSAT field test suggest it is feasible to conduct a statewide needs assessment of school-based transition services using this instrument. Furthermore, the findings from the TSAT align with prior knowledge about school-based transition services, and can be used to inform a state’s goals, priorities and strategies.