Comparison of patient- and clinician-reported outcome measures in lower back rehabilitation: introducing a new integrated performance measure (t2D).

PURPOSE: Patient- and clinician-reported outcome measures (PROMs, CROMs) are used in rehabilitation to evaluate and track the patient's health status and recovery. However, controversy still exists regarding their relevance and validity when assessing a change in health status. METHODS: We retrospectively analyzed the changes in a CROM (Fingertip-To-Floor Test - FTF) and PROMs (ODI, HAQ-DI, NPRS, EQ5D) and the associations between these outcomes in 395 patients with lower back pain (57.2 ± 11.8 years, 49.1% female). We introduced a new way to measure and classify outcome performance using a distribution-based approach (t2D). Outcome measures were assessed at baseline and after 21 days of inpatient rehabilitation. RESULTS: Overall, the rehabilitation (Cohens d = 0.94) resulted in a large effect size outcome. Medium effect sizes were observed for FTF (d = 0.70) and PROMs (d > 0.50). Best performance rating was observed for pain (NPRS). We found that 13.9% of patients exhibited a deterioration in the PROMs, but only 2.3%, in the FTF. The correlation between the PROMs and FTF were low to moderate, with the highest identified for HAQ-DI (rho = 0.30-0.36); no significant correlations could be shown for changes. High consistency levels were observed among the performance scores (t2D) in 68.9% of the patients. CONCLUSIONS: Different and complementary assessment modalities of PROMs and CROMs can be used as valuable tools in the clinical setting. Results from both types of measurements and individual performance assessments in patients provide a valid basis for the meaningful interpretation of the patients' health outcomes. TRIAL REGISTRATION: This clinical study was entered retrospectively on August 14, 2020 into the German Clinical Trials Register (DRKS, registration number: DRKS00022854).

Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education.

Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.

Simulation of Dynamic and Meta-Dynamic Recrystallization Behavior of Forged Alloy 718 Parts Using a Multi-Class Grain Size Model.

Dynamic and meta-dynamic recrystallization occur during forging of alloy 718 aircraft parts and thus change the microstructure during a multistep production route. Since the prediction of the resulting grain structure in a single grain fraction is not able to describe microstructures with bimodal or even multimodal distributions, a multi-class grain size model has been deployed to describe the recrystallization mechanisms during thermomechanical treatments and predict the resulting grain size distributions more accurately. As forging parameters, such as temperature, strain rate and maximum strain influence the flow curve and consequently the recrystallization behavior, a series of double cone compression experiments has been carried out and used to verify and adapt the material parameters for the multi-class grain size model. The recrystallized fractions of the numerical and experimental results are compared and differentiated in view of the recrystallization mechanism, i.e., dynamic and meta-dynamic recrystallization. The strong dependence of the recrystallization kinetics on the initial grain size is highlighted, as well as the influence of different strain rates, which shall represent typical forging equipment.