Background The Evidence-based Policy-Making Act of 2018 requires that Federal agencies use their data to develop statistical evidence to support policy and programmatic decisions.Objective This study assessed Vocational Rehabilitation agency capacity to effectively use their data to inform evidence-based decision-making.Methods The Capacity Survey assessed agency capacity in data management, data visualization and statistical analysis. The survey asked for details about (1) the availability of relevant software programs (e.g., SPSS for statistical analysis), and (2) staff expertise to use that software.Results Results pointed to capacity gaps that would significantly hinder most agencies' application of even a simplified return on investment model. When examining statistical capacity, 60% of agencies responded "not applicable - staff do not have competence in the listed software packages (including SPSS, SAS, R, Python, Stata, and other)" and 71% of respondents said they lacked internal statistical capacity to analyze data using any of the listed programs.Conclusions Results suggested that many state VR agencies lack internal capacity to meet requirements outlined in the Evidence-Based Policy-Making Act of 2018 or regulations related to reporting and performance accountability requirements. Potential solutions to overcome capacity deficits include expanding internal capacity, expanding agency/consultant partnerships, and building cross-agency collaborations.

Deep learning superresolution simultaneous multislice parallel imaging–accelerated knee MRI provided excellent image quality and improved diagnostic performance for articular cartilage defects while maintaining performance for meniscus and ligament tears.

BACKGROUND:Velocity-Based Training (VBT) is an emerging method in resistance training for objectively prescribing and monitoring training intensity and neuromuscular function. Given its growing popularity, assessing the validity and reliability of VBT devices is critical for strength and conditioning coaches. OBJECTIVE:The primary purpose of this review was twofold: (1) to identify and address methodological gaps in current assessments of VBT device validity and reliability, and (2) to propose and apply a novel, multi-layered, criterion-based framework-developed in collaboration with statisticians and domain experts-for evaluating these devices. METHODS:A systematic search was conducted in PubMed, Scopus, and SPORTDiscus following PRISMA guidelines, focusing on original research studies published before February 2024 that assessed VBT device validity or reliability. Out of 568 studies identified, 75 met the inclusion criteria. RESULTS:Among the included studies, 66 investigated device validity and 56 examined reliability, with some studies addressing both aspects. Notably, only 5 of the 66 validity studies met all of the proposed criteria, while just 16 of the 56 reliability investigations satisfied the required statistical thresholds defined by our framework. These findings highlight significant methodological variability and underscore the need for more standardized evaluation practices. CONCLUSIONS:This review systematically evaluated the validity and reliability of various VBT devices and introduced a robust, multi-layered framework for their assessment. By integrating statistician-led and domain expert-led criteria, the framework offers a standardized approach that enhances the precision of device evaluation. Promising tools identified include the GymAware LPT, Perch Motion Capture Single Camera System, Flex optical device, and VmaxPro. Future research should build upon and refine this methodology to further standardize study designs, improve data reporting, and ultimately support more informed decision-making in sports technology and training practice.

Seven-minute threefold parallel imaging–accelerated deep learning super-resolution 3-T shoulder MRI showed robust diagnostic performance, yielding images of high quality and depicting shoulder abnormalities with good to excellent accuracy.

This article contrasts social and taxpayer return on investment measures of the vocational rehabilitation (VR) program in Virginia. To do this, we use the analyses in prior work which demonstrates substantial social return to Virginia’s VR program. Using this estimated model and administrative data on VR clients in Virginia, we simulate earnings that would be realized with and without VR service receipt by each client and estimate the costs of the services provided to each client. Then, given these simulation results, we compute the taxpayer return on investment. Since most VR recipients have a weak attachment to the labor market (i.e., relatively low employment rates and earnings), the relatively large estimated impact of VR on earnings translates into only a small impact on the taxpayer return. That is, the cost of VR is large relative to the lifetime changes in tax receipt. In particular, we estimate that only 29% of VR recipients have a positive taxpayer return.

Hospital decision-makers use predictive models to proactively manage risk of readmission for discharged patients. While predictions from classification models are easily integrated into decision-making processes, it is unclear how to best integrate predictions of the evolution of risk from time-to-event models. We propose a method for summarising predictions of risk over time that produces interpretable components for use in a variety of decision-making processes. The proposed method summarises predictions of risk over time (hazard functions) by approximating them with a parametric smoother. The components of the smoothed approximation can then serve as the basis for decision-making. To demonstrate the proposed summarisation method, we apply it in the specific case of a previously published model for patients discharged from a large teaching hospital on the Gold Coast, Australia. In this context, we describe how the summaries produced by the method could be used to estimate time until a patient reaches a stable, persistent risk level or to stratify patients according to risks of readmission in excess of patient-specific baselines. Our method is anticipated to be valuable in and outside of healthcare for settings where the evolution of risk is important, with specific examples including post-transplantation risk and reinjury risks.

Introduction:Breast cancer is a common form of cancer for women. The goal of this research was to estimate how a breast cancer diagnosis affects a woman's decisions about smoking, alcohol use, and exercise. Methods:Using data from the Panel Study of Income Dynamics on breast cancer diagnosis and lifestyle choices, we estimated how being diagnosed influences smoking, drinking, and exercising habits for more than 8000 women over the period 1999-2011. Results:Controlling for unobserved heterogeneity, persistence in behaviors, and correlation across behaviors, we found that the impact of a diagnosis had a different effect on smoking, drinking, and exercising behaviors. Furthermore, the impact depended upon the recency of the diagnosis. Recently diagnosed women exercised and smoked less-an average woman in our sample reduced exercise by 19% and smoking by 1%. However, women with breast cancer did not change their drinking habits relative to healthy women. Conclusions:A diagnosis of breast cancer impacts lifestyle choices. Women who were diagnosed with breast cancer in the last 5 years exercised and smoked less but did not change their alcohol consumption after a breast cancer diagnosis regardless of when the diagnosis was made. Our approach provides insight into what extent women who are faced with negative information about life expectancy take this into consideration when deciding to engage in risky behaviors that might further affect their survival. Whether to engage in physical activity, drink alcohol, or smoke are choices associated with how to live.

We present a finite-horizon, multiperiod model of a market for durable goods1 (like cars) whose owners have private information about the good’s quality. A good’s quality changes stochastically over time in a way that need not be stationary. Equilibrium is characterized not only by prices and quantities, but also by the quality distributions of the different classes of the goods for sale. We use a fixed-point method to prove the existence of equilibrium.

This paper provides evidence on how racial differences in the classification of learning and intellectual disabilities bias inferences on labor market outcomes of vocational rehabilitation program clients. Estimates using Rehabilitation Services Administration data from Virginia imply that Whites who have learning disabilities have worse labor market outcomes than non-Whites who have learning disabilities. We argue this unusual finding reflects racial differences in how disabilities are classified. Using an endogenous disability classification model, we find substantial biases in the estimated labor market coefficients. At minimum, the estimated White-Black employment gap is biased down by 3.2% and the earnings gap by 10%.

BACKGROUND. The utility of 3-T MRI for diagnosing joint disorders is established, but its performance for diagnosing abnormalities around arthroplasty implants is unclear. OBJECTIVE. The purpose of this study was to compare 1.5-T and 3-T compressed sensing slice encoding for metal artifact correction (SEMAC) MRI for diagnosing peri-prosthetic abnormalities around hip, knee, and ankle arthroplasty implants. METHODS. Forty-five participants (26 women, 19 men; mean age ± SD, 71 ± 14 years) with symptomatic lower extremity arthroplasty (hip, knee, and ankle, 15 each) prospectively underwent consecutive 1.5- and 3-T MRI examinations with intermediate-weighted (IW) and STIR compressed sensing SEMAC sequences. Using a Likert scale, three radiologists evaluated the presence or absence of periprosthetic abnormalities, including bone marrow edema-like signal, osteolysis, stress reaction/fracture, synovitis, and tendon abnormalities and collections; image quality; and visibility of anatomic structures. Statistical analysis included nonparametric comparison and interchangeability testing. RESULTS. For diagnosing periprosthetic abnormalities, 1.5-T and 3-T compressed sensing SEMAC MRI were interchangeable. Across all three joints, 3-T MRI had lower noise than 1.5-T MRI (median IW and STIR scores at 3 T vs 1.5 T, 4 and 4 [range, 2-5 and 3-5] vs 3 and 3 [range, 2-5 and 2-4]; p < .01 for both), sharper edges (median IW and STIR scores at 3 T vs 1.5 T, 4 and 4 [both ranges, 2-5] vs 3 and 3 [range, 2-4 and 2-5]; p < .02 and p < .05), and more effective metal artifact reduction (median IW and STIR scores at 3 T vs 1.5 T, 4 and 4 [range, 3-5 and 2-5] vs 4 and 4 [both ranges, 3-5]; p < .02 and p = .72). Agreement was moderate to substantial for image contrast (IW and STIR, 0.66 and 0.54 [95% CI, 0.41-0.91 and 0.29-0.80]; p = .58 and p = .16) and joint capsule visualization (IW and STIR, 0.57 and 0.70 [range, 0.32-0.81 and 0.51-0.89]; p = .16 and p = .19). The bone-implant interface was more visible at 1.5 T (median IW and STIR scores, 4 and 4 [both ranges, 2-5] at 1.5 T vs 3 and 3 [both ranges, 2-5] at 3 T; p = .08 and p = .58), but periprosthetic tissues had superior visibility at 3 T (IW and STIR, 4 and 4 [both ranges, 3-5] at 3 T vs 4 and 4 [ranges, 2-5 and 3-5] at 1.5 T; p = .07 and p = .19). CONCLUSION. Optimized 1.5-T and 3-T compressed sensing SEMAC MRI are interchangeable for diagnosing periprosthetic abnormalities, although metallic artifacts are larger at 3 T. CLINICAL IMPACT. With compressed sensing SEMAC MRI, lower extremity arthroplasty implants can be imaged at 3 T rather than 1.5 T.

To enable more proactive management of the underlying sources of operational risks in financial institutions, this pre-registered study seeks to improve traditional qualitative approaches to causal factors analysis. A Bayesian network-based approach is used to leverage both incident and operations data to model the probability of operational loss events. The approach is applied and empirically tested in a case study on an Australian insurance company. The outputs from the model go beyond simply identifying key risk drivers to offer risk managers a deeper under-standing of how causal factors influence risk. Insights into the collective effects of causal factors, their relative importance and critical thresholds strategically inform more efficient and effective mitigation decisions, ultimately enhancing firm performance and value.

This work builds on a set of first-mover models of children deciding where to live and its effect on who provides informal care to parents. It presents a model with multiple children and some uncertainty for each child about how younger siblings will behave. The inclusion of uncertainty has large effects on the predictions of the model. For example, once private information is added to the model, 82.7 percent of oldest children would be willing to give up the advantage of first mover to a social planner.

Operational risks are increasingly prevalent and complex to manage in organisations, culminating in substantial financial and non-financial costs. Given the inefficiencies and biases of traditional manual, static and qualitative risk management practices, research has progressed to using data analytics to objectively and dynamically manage risks. However, the variety of operational risks, techniques and objectives researched is not well mapped across industries. This paper thoroughly reviews the emerging research area applying data analytics to operational risk management (ORM) within financial services (FS) and energy and natural resources (ENR). A systematic literature search resulted in 2,538 publications, from which detailed bibliometric and content analyses are performed on 191 studies of relevance. The literature is classified using a novel multi-layered framework, informing critical analyses of the analytics techniques and data employed. Five core themes emerge, relevant to practitioners, researchers, educators and students across any sector: risk identification, causal factors, risk quantification, risk prediction and risk decision-making. Generally, ENR studies focus on identifying causal factors and predicting specific incidents, whereas FS applications are more mature surrounding risk quantification. To conclude, the comprehensive review reveals areas where further research is needed to advance ORM within and beyond FS and ENR, in pursuit of improved decision-making.

We construct a structural model of participation in vocational rehabilitation and labor market outcomes for people with vision impairments. There are multiple services to choose among, and each has different effects on employment and earnings. We estimate negative effects for most service types, leading to surprisingly low rates of return to VR service receipt. The negative returns are strongly affected by large administrative costs.

Our aim was to characterize fluid intake during outdoor team sport training and use generalized additive models to quantify interactions with the environment and performance. Fluid intake, body mass (BM) and internal/external training load data were recorded for male rugby union (n = 19) and soccer (n = 19) athletes before/after field training sessions throughout an 11-week preseason (357 observations). Running performance (GPS) and environmental conditions were recorded each session and generalized additive models were applied in the analysis of data. Mean body mass loss throughout all training sessions was -1.11 +/- 0.63 kg (similar to 1.3%) compared with a mean fluid intake at each session of 958 +/- 476 mL during the experimental period. For sessions >110 min, when fluid intake reached similar to 10-19 mL center dot kg(-1) BM the total distance increased (7.47 to 8.06 km, 7.6%; P = 0.049). Fluid intake above similar to 10 mL center dot kg(-1) BM was associated with a 4.1% increase in high-speed running distance (P < 0.0001). Most outdoor team sport athletes fail to match fluid loss during training, and fluid intake is a strong predictor of running performance. Improved hydration practices during training should be beneficial and we provide a practical ingestion range to promote improved exercise capacity in outdoor team sport training sessions.

BACKGROUND: An important factor embedded within Vocational Rehabilitation (VR) delivery capacity relates to geography, such as distance from the VR office and availability of service providers or community rehabilitation programs. OBJECTIVE: We explored receipt of VR job search and placement services based on distance to an urban center, demographics, and disability variables after controlling for local employment conditions. METHODS: Using 2015 RSA-911 case services data, we used probit to produce estimates for each combination of service and service source (agency and purchased), and Ordinary Least Squares (OLS) and semi-parametric regression to estimate log expenditures for each service category. RESULTS: Being Black or living at a long distance from a metro area increased the probability of receiving agency-based services but lowered the probability of receiving purchased services. Conversely, being older and having less education lowered the probability of receiving agency services but increased the probability of receiving purchased services. Females, Blacks, and those living at a distance greater than 50 miles from a metro area received significantly lower expenditures. CONCLUSION: Systematic differences in the types of services provided call for more in-depth analysis to ensure that policies and procedures are in place to minimize sociodemographic disparities in service delivery and outcomes.

The purpose of our study was to determine the reliability, repeatability, and diagnostic performance of metal artifact reduction magnetic resonance imaging (MARS-MRI) findings for the diagnosis of peri-prosthetic shoulder infection (PSI).

Hospital readmissions lead to unnecessary demand for healthcare resources, greater financial costs, and poorer patient outcomes. These consequences have led hospitals to attempt to identify high-risk patients with predictive models, but research has rarely focused on survival analysis techniques, model applications, and performance measures. This study establishes the uses of survival models to support managerial decision-making for readmissions. First, machine learning and statistical survival techniques are applied, ten of which have not been used in previous readmission research. Secondly, applications of survival models in a decision support capacity are proposed, relating to intervention targeting, follow-up care customisation, and demand forecasting. Thirdly, performance measures for the proposed applications are determined and used for empirical model assessment. These performance measures have not been applied in previous readmission research. The empirical assessment is based on adult admissions to the Emergency Department of Gold Coast University Hospital (n = 46,659) and Robina Hospital (n = 23,976) in Queensland, Australia. The relevant aspects of performance were determined to be discrimination and calibration, as measured by time-dependent concordance and D-Calibration respectively. A range of discrimination and calibration combinations can be achieved by different models, with the Recursively Imputed Survival Tree, Cox regression, and hybrid Cox-ANN techniques being most promising. Survival approaches linking techniques, proposed applications, and performance measurement should be given greater consideration in future healthcare research and in institutions aiming to manage readmissions.

BACKGROUNDThe diagnosis of periprosthetic shoulder infection (PSI) in patients with a painful arthroplasty is challenging. Magnetic resonance imaging (MRI) may be helpful, but shoulder implant-induced metal artifacts degrade conventional MRI. Advanced metal artifact reduction (MARS) improves the visibility of periprosthetic bone and soft tissues. The purpose of our study was to determine the reliability, repeatability, and diagnostic performance of advanced MARS-MRI findings for diagnosing PSI.METHODSBetween January 2015 and December 2019, we enrolled consecutive patients suspected of having PSI at our academic hospital. All 89 participants had at least 1-year clinical follow-up and underwent standardized clinical, radiographic, and laboratory evaluations and advanced MARS-MRI. Two fellowship-trained musculoskeletal radiologists retrospectively evaluated the advanced MARS-MRI studies for findings associated with PSI in a blinded and independent fashion. Both readers repeated their evaluations after a 2-month interval. Interreader reliability and intrareader repeatability were assessed with κ coefficients. The diagnostic performance of advanced MARS-MRI for PSI was quantified using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). When applying the International Consensus Meeting (ICM) 2018 criteria, of the 89 participants, 22 (25%) were deemed as being infected and 67 (75%) were classified as being not infected (unlikely to have PSA and not requiring a surgical procedure during 1-year follow-up).RESULTSThe interreader reliability and intrareader repeatability of advanced MARS-MRI findings, including lymphadenopathy, joint effusion, synovitis, extra-articular fluid collection, a sinus tract, rotator cuff muscle edema, and periprosthetic bone resorption, were good (κ = 0.61 to 0.80) to excellent (κ > 0.80). Lymphadenopathy, complex joint effusion, and edematous synovitis had sensitivities of >85%, specificities of >90%, odds ratios of >3.6, and AUC values of >0.90 for diagnosing PSI. The presence of all 3 findings together yielded a PSI probability of >99%, per logistic regression analysis.CONCLUSIONSOur study shows the clinical utility of advanced MARS-MRI for diagnosing PSI when using the ICM 2018 criteria as the reference standard. Although the reliability and diagnostic accuracy were high, these conclusions are based on our specific advanced MARS-MRI protocol interpreted by experienced musculoskeletal radiologists. Investigations with larger sample sizes are needed to confirm these results.LEVEL OF EVIDENCEDiagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.