Deggendorf Institute of Technology

Cerebral cavernous malformations (CCMs) are clusters of thin-walled enlarged blood vessels in the central nervous system that are prone to recurrent hemorrhage and can occur in both sporadic and familial forms. The familial form results from loss-of-function variants in the CCM1, CCM2, or CCM3 gene. Despite a better understanding of CCM pathogenesis in recent years, it is still unclear why CCM3 mutations often lead to a more aggressive phenotype than CCM1 or CCM2 variants. By combining high-throughput differentiation of blood vessel organoids from human induced pluripotent stem cells (hiPSCs) with a CCM1, CCM2, or CCM3 knockout, single-cell RNA sequencing, and high-content imaging, we uncovered both shared and distinct functions of the CCM proteins. While there was a significant overlap of differentially expressed genes in fibroblasts across all three knockout conditions, inactivation of CCM1, CCM2, or CCM3 also led to specific gene expression patterns in neuronal, mesenchymal, and endothelial cell populations, respectively. Taking advantage of the different fluorescent labels of the hiPSCs, we could also visualize the abnormal expansion of CCM1 and CCM3 knockout cells when differentiated together with wild-type cells into mosaic blood vessel organoids. In contrast, CCM2 knockout cells showed even reduced proliferation. These observations may help to explain the less severe clinical course in individuals with a pathogenic variant in CCM2 and to decode the molecular and cellular heterogeneity in CCM disease. Finally, the excellent scalability of blood vessel organoid differentiation in a 96-well format further supports their use in high-throughput drug discovery and other biomedical research studies. Supplementary Information The online version contains supplementary material available at 10.1007/s10456-025-09985-5.

Background Total hip arthroplasty (THA) is a highly successful orthopedic procedure, with numerous meta‐analyses published to optimize its outcomes. However, the reliability of their results and conclusions depends heavily on the use of appropriate statistical methods. Therefore, the aim was to test the reliability of statistical methods in meta‐analyses of THA by examining the degree of heterogeneity, the effect of different between‐study variance estimators, and the equality of sample size of pooled primary studies. Methods The literature was systematically searched in PubMed from January 1, 2022, to December 31, 2023, for meta‐analyses on THA. The quality of the meta‐analyses was assessed using the revised Measurement Tool to Assess Systematic Reviews (AMSTAR 2). All meta‐analyses were recalculated using eight different heterogeneity estimators. The following indicators were considered: inequality of patient numbers, proportion of random‐effects and fixed‐effects models, heterogeneity with I² value, ratio of effect sizes (RES), ratio of confidence interval width (RCIW), and the number of significant results. Mixed linear regression was then used to analyze whether the effect sizes and CIW were significantly different using different heterogeneity estimators. Finally, all examined meta‐analyses were recalculated using the eight heterogeneity estimators and the Hartung–Knapp (HK) adjustment. Results Of the 24 meta‐analyses examined, 15 reported an outcome using a mean difference and 20 reported an outcome using an odds ratio. The quality assessment identified 10 meta‐analyses of high quality, 7 of moderate quality, 4 of low quality, and 3 of critically low quality. The significance of the examined meta‐analyses varied considerably depending on the heterogeneity estimators used. In particular, the DerSimonian and Laird and Hunter–Schmidt heterogeneity estimators tended to produce false‐positive results. The meta‐analyses examined generally did not use HK adjustment. This effect is amplified when combined with the weak DerSimonian and Laird heterogeneity estimator, which were used in almost all examined meta‐analyses. Conclusion Without HK adjustment, the results depend strongly on the heterogeneity estimator chosen and there is a risk of false positives, especially for the widely used DerSimonian and Laird heterogeneity estimator. For HK adjustment, the choice of heterogeneity estimator seems to play a less important role. We recommend the use of more reliable heterogeneity estimators as well as the HK adjustment as a measure to improve the statistical methodology of meta‐analyses. This study highlights the critical need for improved statistical rigor in meta‐analyses of THA, ensuring more reliable evidence for clinical decision‐making and guideline development.

Objective Iliopsoas impingement (IPI) syndrome is a significant complication following total hip arthroplasty (THA), often leading to pain and reduced hip function. Despite its clinical relevance, the optimal treatment strategy remains unclear, with varying success rates reported across different interventions. This study aims to compare four treatment options (endoscopic, acetabular cup revision, open tenotomy and conservative management) for patients with IPI syndrome after THA by comparing outcomes in terms of function, pain, complications, and reoperations through a multilevel meta‐analysis. Methods A literature search was conducted in the following databases until 30 November 2024: PubMed, CENTRAL, Epistemonikos, and Embase. A frequentist multilevel meta‐analysis was performed using a random effects model with an inverse variance and restricted maximum likelihood heterogeneity estimator with Hartung‐Knapp adjustment. Means with 95% confidence intervals (CIs) were calculated separately in the four treatment groups. Then, a test for subgroup differences in multilevel meta‐analysis was performed to determine whether there is a statistically significant difference between the means of the four groups. Results The systematic review included 15 studies with 425 patients. The test for subgroup differences showed no statistically significant difference between the four treatment subgroups in Harris Hip Score (HHS) post‐intervention (F = 2.0; df = 3, 7; p = 0.20), in HHS difference (F = 2.0; df = 3, 6; p = 0.22), and in functional minimal clinically important differences (MCID) post‐intervention (F = 1.0; df = 3, 2; p = 0.42). The conservative management group exhibited the lowest mean HHS (70.3 points). Conclusions Surgical interventions, including endoscopic tenotomy, acetabular cup revision, and open tenotomy, are effective in achieving meaningful functional improvements in IPI patients. While conservative management was the least effective of all treatment groups, the differences did not reach statistical significance.

Background: Activities of Daily Living (ADLs) are essential tasks performed at home and used in healthcare to monitor sedentary behavior, track rehabilitation therapy, and monitor chronic obstructive pulmonary disease. The Barthel Index, used by healthcare professionals, has limitations due to its subjectivity. Human activity recognition (HAR) is a more accurate method using Information and Communication Technologies (ICTs) to assess ADLs more accurately. This work aims to create a singular, adaptable, and heterogeneous ADL dataset that integrates information from various sources, ensuring a rich representation of different individuals and environments. Methods: A literature review was conducted in Scopus, the University of California Irvine (UCI) Machine Learning Repository, Google Dataset Search, and the University of Cauca Repository to obtain datasets related to ADLs. Inclusion criteria were defined, and a list of dataset characteristics was made to integrate multiple datasets. Twenty-nine datasets were identified, including data from various accelerometers, gyroscopes, inclinometers, and heart rate monitors. These datasets were classified and analyzed from the review. Tasks such as dataset selection, categorization, analysis, cleaning, normalization, and data integration were performed. Results: The resulting unified dataset contained 238,990 samples, 56 activities, and 52 columns. The integrated dataset features a wealth of information from diverse individuals and environments, improving its adaptability for various applications. Conclusions: In particular, it can be used in various data science projects related to ADL and HAR, and due to the integration of diverse data sources, it is potentially useful in addressing bias in and improving the generalizability of machine learning models.

Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing an individual’s autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating early dependency detection, all of which are relevant components of personalized health and social care. However, the automatic classification of ADLs from sensor data remains challenging due to high variability in human behavior, sensor noise, and discrepancies in data acquisition protocols. These challenges limit the accuracy and applicability of existing solutions. This study details the modeling and evaluation of real-time ADL classification models based on batch learning (BL) and stream learning (SL) algorithms. Methods: The methodology followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models were trained with a comprehensive dataset integrating 23 ADL-centric datasets using accelerometers and gyroscopes data. The data were preprocessed by applying normalization and sampling rate unification techniques, and finally, relevant sensor locations on the body were selected. Results: After cleaning and debugging, a final dataset was generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared models trained with BL and SL algorithms, evaluating their performance under various classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics. Finally, a mobile application was developed to classify ADLs in real time (feeding data from a dataset). Conclusions: The outcome of this study can be used in various data science projects related to ADL and Human activity recognition (HAR), and due to the integration of diverse data sources, it is potentially useful to address bias and improve generalizability in Machine Learning models. The principal advantage of online learning algorithms is dynamically adapting to data changes, representing a significant advance in personal autonomy and health care monitoring.

Background/Objectives: Posterior pelvic ring fractures are severe injuries requiring surgical stabilization, often through sacroiliac (SI) screw fixation. However, improper screw placement poses risks of neurovascular injury and implant failure. Defining a precise safe zone for screw placement is crucial to improving surgical accuracy and reducing complications. Methods: A computational study was conducted using a CT scan of a 75-year-old male patient to establish a safe zone for SI screw placement. Manual segmentation and 3D modeling techniques were used to analyze bone density distribution. A 2D lateral projection of the sacrum was generated to identify high-density regions optimal for screw placement. While the general principle of targeting areas of higher bone density for screw insertion is well established, this study introduces a novel computational method to define and visualize such a safe zone. The resulting region, termed the Ramadanov–Zabler Safe Zone, was delineated based on this analysis to ensure maximal intraosseous fixation with minimal risk of cortical breaches. Results: A high-resolution 3D model of the sacral region was successfully generated. Standard thresholding methods for segmentation proved ineffective due to low bone density, necessitating a freehand approach. The derived 2D projection revealed regions of higher bone density, which were defined as the Ramadanov-Zabler Safe Zone for screw insertion. This zone correlates with areas providing the best structural integrity, thereby reducing risks associated with screw misplacement. Additionally, intraoperative and postoperative imaging from a representative case is included to illustrate the translational feasibility of the proposed technique. Conclusions: The Ramadanov–Zabler Safe Zone offers a reproducible, CT-based computational approach to guide for SI screw placement, enhancing surgical precision and patient safety. This CT-based computational approach provides a standardized reference for preoperative planning, minimizing neurovascular complications and improving surgical outcomes. This pilot technique is supported by preliminary clinical imaging that demonstrates feasibility for intraoperative application. Further validation across diverse patient populations is recommended to confirm its clinical applicability.

Zielsetzung: Dieser Arbeit liegt folgende Forschungsfrage zugrunde: „Welche Interventionen zur Begegnung der Bedarfe und Bedürfnisse von Familien nach intrauteriner oder perinataler Verlusterfahrung eines Kindes sind bereits empirisch erforscht?“ Ziel ist es, einen Überblick über vorhandene Forschungsarbeiten zu entwickeln, die Interventionen zu gruppieren und Forschungslücken zu identifizieren. Materialien und Methoden: Ein Scoping Review wurde durchgeführt. Die systematische Suche erfolgte im Juli 2024 in den Datenbanken Medline, CINAHL und Cochrane. Eingeschlossen wurden empirische Originalarbeiten, in welchen Interventionen für Eltern und Geschwisterkinder nach intrauterinem oder perinatalem Verlust eines Kindes erforscht wurden. Ausgeschlossen wurden Studien, welche sich lediglich mit den Erfahrungen oder den von betroffenen Eltern gewünschten Vorgehensweisen beschäftigten. Das Screening und die Datenextraktion wurden von zwei Reviewern unabhängig durchgeführt. Die Synthese basiert auf thematisch gebildeten Clustern, welche nachfolgend narrativ vorgestellt werden [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]. Ergebnisse: Von den identifizierten 3.502 Treffern wurden 97 für das Volltextscreening herangezogen und insgesamt 25 in das Scoping Review inkludiert. Aus diesen konnten sechs zentrale Cluster gebildet werden. Der Cluster ´Interaktion mit dem Sternenkind´ beinhaltet Interventionen wie das Sehen und Halten des Kindes oder das Anfertigen von Fotos. In einem weiteren Cluster wird die ´Geburtshilfliche Versorgung´ in Protokollen und Leitlinien zur Geburt oder zur Trauerbewältigung zusammengefasst. Auch existieren ´Digitale Unterstützungsangebote´, beispielsweise webbasierte Yoga-Kurse oder verschiedene Telefonservices. Die ´Psychosoziale Unterstützung der Eltern´ umfasst angebotene Interventionen durch Gesundheitspersonal, Angehörige, Freunde oder Glaubensvertretern. Auch einige wenige Maßnahmen zur ´Psychosozialen Unterstützung der Geschwisterkinder´ werden in der Literatur beschrieben. Zuletzt wird auch das Vorgehen zur ´Versorgung in der Folgeschwangerschaft nach perinatalem Verlust´ in vorhandenen Studien erläutert. Zusammenfassung: Die identifizierten Studien befassen sich mit einer Vielzahl von Interventionen. Diese decken einen Teil der mittel- und langfristigen Bedarfe und Bedürfnisse nach intrauterinem oder perinatalem Verlust eines Kindes ab und berücksichtigen die Versorgung nach der Geburt bis hin zur Unterstützung bei Folgeschwangerschaften. Die beschriebenen Interventionen sind hauptsächlich für Eltern ausgelegt, zu Maßnahmen für Geschwisterkinder oder weitere Familienmitglieder liegt aktuell nur wenig Evidenz vor. Auf Grundlage der Bedarfe und Bedürfnisse von Eltern werden in der Literatur weitere Interventionen (z.B. Laktationsberatung oder Entscheidungsunterstützung für oder gegen eine Autopsie) gefordert. Diese bedarf es in zukünftiger Forschung zu entwickeln, zu implementieren und abschließend zu evaluieren. Registrierung: https://doi.org/10.17605/OSF.IO/W9AU5

Zielsetzung: Das Ziel dieser Arbeit ist es, einen Überblick über Forschungsarbeiten zu den Erfahrungen von Bezugspersonen lebensverkürzt erkrankter Kinder während eines Aufenthalts im Hospiz zu schaffen. Das Literaturstudium bildet zudem das Fundament für eine folgende qualitative Untersuchung. Die Forschungsfrage lautet: „Welche Erfahrungen von Bezugspersonen während der Versorgung ihrer zugehörigen lebensverkürzt erkrankten Kinder im Hospiz wurden bereits empirisch identifiziert?“ Materialien und Methoden: Ein Scoping Review wurde durchgeführt. Die systematische Suche erfolgte im Juli 2024 in den Datenbanken Medline, CINAHL und Cochrane. Eingeschlossen wurden empirische Originalarbeiten, in welchen die Erfahrungen von Bezugspersonen von lebensverkürzt erkrankten Kindern zwischen null und 18 Jahren in pädiatrischen Hospiz- und Palliativversorgungseinrichtungen stationär oder ambulant erforscht wurden. Studien zur palliativen Versorgung auf Intensivstationen oder zur häuslichen Betreuung wurden ausgeschlossen. Das Screening und die Datenextraktion wurden von zwei Reviewern unabhängig durchgeführt. Die Synthese basiert auf thematisch gebildeten Clustern, welche nachfolgend narrativ vorgestellt werden. Ergebnisse: Von den identifizierten 3.845 Treffern wurden 53 für das Volltextscreening herangezogen und insgesamt 21 in das Scoping Review inkludiert. Aus diesen konnten neun zentrale Cluster gebildet werden. Die Bezugspersonen berichten von einer körperlichen und emotionalen Belastung durch die Betreuung ihres lebensverkürzt erkrankten Kindes, im Cluster ´Entlastung für die Familien´ werden positive Erfahrungen der Familien hierzu dargestellt. ´Hilfe annehmen und Vertrauen aufbauen´ wurde als weiteres Cluster identifiziert, ebenso wie das Erleben von ´Zugehörigkeit und Geborgenheit´ und dem Wahrnehmen von ´Aktivitäten und gemeinsamer Zeit´ im Hospiz. Familien teilen die Erfahrung, dass auch ´Geschwister im Fokus´ stehen und ´bleibende Erinnerungen´ gesammelt werden können. In einem weiteren Cluster zeigen sich die ´Herausforderungen im Hospizalltag´ wie z.B. fehlendes Equipment, organisatorischer Aufwand oder routinierte, nicht auf die Familie abgestimmte Arbeits- und Tagesabläufe. Zuletzt wurden auch Erfahrungen rund um das Versterben des Kindes im Hospiz identifiziert, welche zu den Clustern ´End-of-life Care im Hospiz´ und ´Zeit für den letzten Abschied´ zusammengefasst wurden [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]. Zusammenfassung: Familien mit lebensverkürzt erkrankten Kindern erleben eine Vielzahl an Erfahrungen im Hospiz. Das Gesundheitspersonal muss dabei die Versorgung anhand der Bedürfnisse von Bezugspersonen und dem lebensverkürzt erkrankten Kind koordinieren. Auf Basis der Erfahrungen der Familien, die in dieser Literaturarbeit und in der folgenden qualitativen Untersuchung identifiziert werden, sollen Handlungsempfehlungen für das Gesundheitspersonal entwickelt werden, um die Versorgung im Hospiz weiter zu optimieren. Registrierung: https://doi.org/10.17605/OSF.IO/QXPCS

Background The aim of this study was to compare the performance of artificial intelligence (AI) in detecting distal radius fractures (DRFs) on plain radiographs with the performance of human raters. Methods We retrospectively analysed all wrist radiographs taken in our hospital since the introduction of AI-guided fracture detection from 11 September 2023 to 10 September 2024. The ground truth was defined by the radiological report of a board-certified radiologist based solely on conventional radiographs. The following parameters were calculated: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN), accuracy (%), Cohen’s Kappa coefficient, F1 score, sensitivity (%), specificity (%), Youden Index (J Statistic). Results In total 1145 plain radiographs of the wrist were taken between 11 September 2023 and 10 September 2024. The mean age of the included patients was 46.6 years (± 27.3), ranging from 2 to 99 years and 59.0% were female. According to the ground truth, of the 556 anteroposterior (AP) radiographs, 225 cases (40.5%) had a DRF, and of the 589 lateral view radiographs, 240 cases (40.7%) had a DRF. The AI system showed the following results on AP radiographs: accuracy (%): 95.90; Cohen’s Kappa: 0.913; F1 score: 0.947; sensitivity (%): 92.02; specificity (%): 98.45; Youden Index: 90.47. The orthopedic surgeon achieved a sensitivity of 91.5%, specificity of 97.8%, an overall accuracy of 95.1%, F1 score of 0.943, and Cohen’s kappa of 0.901. These results were comparable to those of the AI model. Conclusion AI-guided detection of DRF demonstrated diagnostic performance nearly identical to that of an experienced orthopedic surgeon across all key metrics. The marginal differences observed in sensitivity and specificity suggest that AI can reliably support clinical fracture assessment based solely on conventional radiographs.

The new digital health paradigms have shifted to be more patient-centric, preventive, predictive, and personalized to more accurately and precisely cater to the patient’s needs. The dawn of novel innovative technologies revolutionizing the digital health sector across the globe has created multiple value creations and research opportunities. The following research focuses on studying innovation diffusion in the digital health sector and its effect on the success of startups via analysis of the business models. It is essential to understand the significant proportionality of innovation to the success of digital health startups in delivering quality care to users/ patients without compromising quality or failing to create business value in the current competitive European market.

Rapid and reliable automated identification of wood species can be a boon for applications across wood scientific context including forestry and biodiversity conservation, as well as in an industrial context via requirements for timber trade regulations. However, robust machine learning classifiers must be properly analyzed and immunized against domain shift effects. These can degrade the automated system performance for input data variations occurring in many scenarios. This work analyses the domain shift generated by using two differing sub-micro-scale and micro-scale computed tomography setups in the context of deep learning based binary wood classification from volumetric image data. Further, we examine several mitigation strategies and propose data- and model-level intertwined strategies to effectively minimize the performance domain gap. Core elements of the strategy include the combined usage of phase-correction methods, low-pass pyramid representation of the data and model normalization and regularization approaches. Vanishing domain performance differences led to the conclusion that the combined strategy ultimately prompted the model to learn robust features. These features are discriminative for input data from both sub-micro-system and micro-system domains, despite the substantial differences in data acquisition setup that propagate into fundamental image quality metrics like resolution, contrast and signal-to-noise ratio.

Twin robotic X-ray computed tomography (CT) refers to CT systems in which two robotic arms are used to independently move the X-ray source and the X-ray detector around the object. This setup enables flexible CT scans by using robots to move the X-ray source and the X-ray detector around an object’s region of interest. This allows scans of large objects, image quality optimization and scan time reduction. Despite these advantages, robotic CT systems still face challenges that limit their widespread adoption. This paper discusses the state of twin robotic CT and its current main challenges. These challenges include the optimization of scanning trajectories, precise geometric calibration and advanced 3D reconstruction techniques.

Purpose IGF-I is a well-established biomarker for detecting abnormalities in the growth hormone axis and evaluating effectiveness of growth hormone (GH) treatment. Common age-related diseases, such as sarcopenia are associated with impairments in the GH axis, making targeted GH therapy a potential treatment option. Nonetheless, data on the biological variation of IGF-I in older patients are missing, potentially leading to inaccurate interpretation of IGF-I concentrations in the diagnostic and therapeutic process. Our study aims to address this gap. Methods We conducted a retrospective analysis of IGF-I concentrations measured in samples from the geriatric outpatient facility of the Ludwig-Maximilians-University Hospital, Munich and the respective patient data from the MUnich SArcopenia Registry (MUSAR). Using a mixed-effects model, we estimated the intraindividual biological coefficient of variation (CVi). We calculated the Reference Change Values (RCV) and the Index of Individuality (II). Results 246 serum samples from 89 patients (mean age 83 years, range 70–97) were analyzed. The CVi ranged from 13.4 to 15.6%, with a mean of 14.7%. RCV was 30.7% for a decrease and 44.3% for an increase in IGF-I concentrations. The II was 0.44. Conclusion The CVi of IGF-I in our cohort differs from that previously described in younger and healthier populations and is therefore crucial for identifying significant changes in this geriatric cohort. The high degree of individuality also supports the application of personalized reference intervals. Our study provides data on the biological variation of IGF-I concentrations in geriatric patients; the calculated RCVs have the potential to refine interpretation.

Background Internet-based cognitive behavioral therapy for insomnia (iCBT-I) provides flexibility but requires significant time and includes potentially challenging components such as sleep restriction therapy. This raises questions about its incremental effectiveness compared to less demanding minimal interventions such as sleep hygiene psychoeducation. Objective This study aimed to assess the incremental efficacy of self-guided iCBT-I with optional on-demand feedback for university students with insomnia compared to a single session of digital psychoeducation on sleep hygiene. Methods In a randomized controlled trial, 90 students with insomnia (Insomnia Severity Index ≥10) were randomly allocated to self-help–based iCBT-I (45/90, 50%) or one session of digital sleep hygiene psychoeducation with stimulus control instructions (active control group [aCG]: 45/90, 50%). The self-help–based iCBT-I consisted of 6 sessions on psychoeducation, sleep restriction, and stimulus control, including written feedback on demand from an eCoach. Assessments occurred at baseline (T1), 8 weeks after treatment (T2), and at a 6-month follow-up (T3) via web-based self-assessment and diagnostic telephone interviews. The primary outcome was insomnia severity at T2. Analyses of covariance were conducted in an intention-to-treat sample. Secondary outcomes included diagnoses of insomnia and major depression, sleep quality, sleep efficiency, worrying, recovery experiences, recovery activities, presenteeism, procrastination, cognitive irritation, and recuperation in sleep. Results There was no difference in insomnia severity at T2 between the iCBT-I group (mean 11.27, SD 5.21) and aCG group (mean 12.36, SD 4.16; F1,989.03=1.12; P=.29; d=–0.26; 95% CI 0.68 to 0.17). A significant difference emerged at T3 (iCBT-I: mean 9.43, SD 5.36; aCG: mean 12.44, SD 5.39; F1,426.15=4.72; P=.03), favoring iCBT-I with a medium effect (d=–0.57; 95% CI 1.07 to –0.06). Most secondary outcomes revealed no significant differences between the groups. In total, 51% (23/45) of participants in the iCBT-I group completed all 6 sessions, and 69% (31/45) completed the 4 core sessions. Conclusions In the short term, students might benefit from low-intensity, easily accessible digital sleep hygiene psychoeducation or iCBT-I. However, it appears that iCBT-I offers superiority over sleep hygiene psychoeducation in the long term. Trial Registration German Clinical Trials Register DRKS00017737; https://drks.de/search/de/trial/DRKS00017737

Zusammenfassung Da sich international und national die Anforderungen an Physiotherapeut*innen in der Gesundheitsversorgung ändern, sind die Qualifikationsprofile der Gesundheitsfachberufe neu zu denken. Im deutschen Gesundheits- und Hochschulsystem gibt es noch keinen Konsens von Fachverantwortlichen an den Hochschulen zu den Kernkompetenzen, die im Rahmen eines Physiotherapiestudiums auf Bachelorebene erreicht werden sollen. Entwicklung eines Qualifikationsprofils. Dieser Diskussionsbeitrag geht der Frage nach, welche Kernkompetenzen für einen Abschluss eines Studiums Bachelor of Science (B.Sc.) Physiotherapie im Sinne eines Mindeststandards erworben werden sollten. Auf Basis einer Datenbankrecherche und freien Internetrecherche wurden kompetenzorientierte Rahmenkonzepte für die Physiotherapie zusammengetragen und Daten zu Rollen bzw. Domänen physiotherapeutischen Handelns und den dazugehörigen Kernkompetenzen extrahiert und gegenübergestellt. In einem Workshop diskutierten 6 Expertinnen unterschiedlicher Hochschulen die Rechercheergebnisse, legten Domänen physiotherapeutischen Handelns fest und entwickelten auf dieser Grundlage ein Qualifikationsprofil. Dieser Entwurf wurde anschließend durch physiotherapeutische Fachvertreter*innen des Fachbereichstag Therapiewissenschaften (FBTT) (n = 22) und durch den Vorstand der Deutschen Gesellschaft für Physiotherapiewissenschaft (DGPTW) im schriftlichen Umlauf konsentiert und verabschiedet. Das Qualifikationsprofil B.Sc. Physiotherapie umfasst 5 Domänen (Professionalität, Wissenschaft und Forschung, Praktisches Handeln, Management und Leadership, Lernen und Entwicklung), welchen 35 Kernkompetenzen zugeordnet sind. Das vorliegende Qualifikationsprofil beschreibt erstmalig für Deutschland professionsspezifische Kernkompetenzen eines B.Sc. Physiotherapie. Diese dienen derzeit der Fachgemeinschaft zur Diskussion sowie als Basis für weitere Ausdifferenzierungen und curriculare Entwicklungen. Sie können weiterhin von unterschiedlichen Zielgruppen als Orientierungsrahmen verwendet werden.

Aims Femoroacetabular impingement (FAI) is a serious cause of hip pain with loss of function, and development of osteoarthritis of the hip. The aim of this multilevel meta-analysis of randomized controlled trials (RCTs) was to evaluate the outcomes of FAI patients treated conservatively compared with those treated with hip arthroscopy (HAS). Methods A systematic literature search of PubMed, CENTRAL of the Cochrane Library, Epistemonikos, and Embase databases was conducted up to 30 June 2024. In a frequentist multilevel meta-analysis with random effects model, means with 95% CIs were calculated separately in the conservative treatment subgroup and the HAS subgroup. A test for subgroup differences in meta-analysis was then performed to determine whether there was a statistically significant difference between the means of the two subgroups. Clinical assessment was based on Harris Hip Score (HHS), the International Hip Outcome Tool (iHOT), the Hip disability and Osteoarthritis Outcome Score (HOOS), the Hip Outcome Score Activities of Daily Living (HOS-ADL), and visual analogue scale (VAS) for pain. Results A total of 21 RCTs, including 674 patients in the conservative treatment subgroup and 1,125 patients in the HAS subgroup, met the inclusion criteria. The test for subgroup differences showed that the HAS subgroup had a statistically significant 6.5-point higher HHS ≤ 12 months post-intervention ( F = 12.8; df = 1.5; p = 0.016) and a statistically significant 9.8-point higher iHOT ≤ 24 months post-intervention ( F = 5.3; df = 1.1; p = 0.035) than the conservative treatment subgroup. Other functional (HOOS, HOS) and pain (VAS, NRS) outcome parameters analyzed did not show statistically significant differences. Conclusion This multilevel meta-analysis of 21 RCTs with a total of 1,799 FAI patients showed a statistically significant higher HHS ≤ 12 months post-intervention and iHOT ≤ 24 months post-intervention, favouring the HAS subgroup compared to the conservative treatment subgroup, without reaching minimal clinically important differences (MCIDs). Cite this article: Bone Jt Open 2025;6(4):480–498.

Optimizing phase‐contrast micro‐computed tomography (µCT) for a given object is not trivial if the radiation is polychromatic and the object multi‐material. This study demonstrates how an optimal combination of propagation distance and mean energy (set by attenuation filters) may be derived for such an object (an electromotor scanned on beamline BM18 at ESRF in Grenoble, France). In addition to appropriate image quality metrics, it is mandatory to define a task. In that respect, raising Emean from 100 keV to 164 keV mitigates beam hardening by metal parts, yet raising Emean further to 230 keV deteriorates CNR² (where CNR is contrast‐to‐noise ratio) due to higher image noise. Propagation distances between d = 2 m and 25.3 m are evaluated crosswise with energy. While longer propagation distances generally yield higher CNR², shorter distances appear favorable when discerning plastic near metal parts. SNR² (where SNR is signal‐to‐noise ratio) power spectra and modulation transfer (MTF) are evaluated independently from two‐dimensional projections supporting volume image analysis for which image sharpness depends strongly on the digital filters (Paganin and Wiener) which are applied along with filtered back‐projection. In summary, optimizing synchrotron µCT scans remains a very complex task which differs from object to object. A physically accurate model of the complete imaging process may not only allow for optimization by simulation but also ideally improve CT image reconstruction in the near future.

The advancement of digital maturity of healthcare organizations and health systems hinges on the potential of digital tools to empower healthcare professionals. This empowerment extends beyond care delivery to encompass the use of digital technologies for administrative functions, ensuring seamless operations and automated clinical workflows. To achieve a digitally empowered healthcare workforce, building healthcare professionals’ capacity, confidence, and trust in digital tools remains imperative amidst resource constraints faced by healthcare organizations. Integrating data and artificial intelligence within care pathways and clinical management tools offers avenues to enhance healthcare effectiveness and overall improve the health workforce’s performance. As the health workforce evolves, including diverse stakeholders in the digital transformation process becomes paramount to the nurturing of integrated and sustainable digital health ecosystems. This article delineates three user groups—Digital Health Workforce, Health Workforce, and Empowered Citizens—each with defined roles crucial for improved healthcare outcomes and accelerated adoption of digital technologies within clinical practice. Each user group necessitates both technological investment and cultural shift, underlining the need for distinct training and upskilling programs. The authors propose personalized strategies for the further development of the user groups and outline scenarios for fostering workforce-driven digital maturity to ensure healthcare’s resilience in the new demographic era.