Kooperation der Lernorte in der beruflichen Bildung (KOLIBRI). Abschlussbericht des Programmträgers zum BLK-Programm

Der Abschlussbericht stellt den (vorläufigen) Endpunkt intensiver Forschungen zum Thema "Lernortkooperation" dar. Im Zeitraum von Oktober 1999 bis Dezember 2003 wurden 28 Modellversuche, die zum Thema Lernortkooperation arbeiteten, im Programm KOLIBRI ("Kooperation der Lernorte in der beruflichen Bildung") zusammengefasst. Die einzelnen Forschungsvorhaben untersuchten die verschiedenen Facetten von Lernortkooperation und konzipierten praktische Lösungen für die unterschiedlichsten Probleme. (DIPF/Orig.

A Primer on HIBS -- High Altitude Platform Stations as IMT Base Stations

Mobile communication via high-altitude platforms operating in the stratosphere is an idea that has been on the table for decades. In the past few years, however, with recent advances in technology and parallel progress in standardization and regulatory bodies like 3GPP and ITU, these ideas have gained considerable momentum. In this article, we present a comprehensive overview of HIBS - High Altitude Platform Stations as IMT Base Stations. We lay out possible use cases and summarize the current status of the development, from a technological point of view as well as from standardization in 3GPP, and regarding spectrum aspects. We then present preliminary system level simulation results to shed light on the performance of HIBS. We conclude with pointing out several directions for future research.Comment: 7 pages, 4 figure

MULTIPLE CEREBRAL METASTASES MIMICKING WERNICKE'S ENCEPHALOPATHY IN A CHRONIC ALCOHOLIC

Aims: Alcohol dependent patients in withdrawal display a wide spectrum of neurological and neuropsychological symptoms that complicate diagnosis. We report the case of a 53-year-old male alcoholic with disorientation, ataxia and nystagmus in alcohol withdrawal probably due not to initial supposed Wernicke's encephalopathy (WE) but rather due to multiple cerebral metastases of a non-small cell cancer of the lung. Results: The findings illustrate the importance of initially maintaining a tentative attitude toward causation of symptoms and the role of brain imaging in formulating an accurate diagnosi

Proof of concept: Predicting distress in cancer patients using back propagation neural network (BPNN)

Background: Research findings suggest that a significant proportion of individuals diagnosed with cancer, ranging from 25% to 60%, experience distress and require access to psycho-oncological services. Until now, only contemporary approaches, such as logistic regression, have been used to determine predictors of distress in oncological patients. To improve individual prediction accuracy, novel approaches are required. We aimed to establish a prediction model for distress in cancer patients based on a back propagation neural network (BPNN). Methods: Retrospective data was gathered from a cohort of 3063 oncological patients who received diagnoses and treatment spanning the years 2011-2019. The distress thermometer (DT) has been used as screening instrument. Potential predictors of distress were identified using logistic regression. Subsequently, a prediction model for distress was developed using BPNN. Results: Logistic regression identified 13 significant independent variables as predictors of distress, including emotional, physical and practical problems. Through repetitive data simulation processes, it was determined that a 3-layer BPNN with 8 neurons in the hidden layer demonstrates the highest level of accuracy as a prediction model. This model exhibits a sensitivity of 79.0%, specificity of 71.8%, positive predictive value of 78.9%, negative predictive value of 71.9%, and an overall coincidence rate of 75.9%. Conclusion: The final BPNN model serves as a compelling proof of concept for leveraging artificial intelligence in predicting distress and its associated risk factors in cancer patients. The final model exhibits a remarkable level of discrimination and feasibility, underscoring its potential for identifying patients vulnerable to distress

Towards identifying cancer patients at risk to miss out on psycho-oncological treatment via machine learning

Objective: In routine oncological treatment settings, psychological distress, including mental disorders, is overlooked in 30% to 50% of patients. High workload and a constant need to optimise time and costs require a quick and easy method to identify patients likely to miss out on psychological support. Methods: Using machine learning, factors associated with no consultation with a clinical psychologist or psychiatrist were identified between 2011 and 2019 in 7,318 oncological patients in a large cancer treatment centre. Parameters were hierarchically ordered based on statistical relevance. Nested resampling and cross validation were performed to avoid overfitting. Results: Patients were least likely to receive psycho-oncological (i.e., psychiatric/psychotherapeutic) treatment when they were not formally screened for distress, had inpatient treatment for less than 28 days, had no psychiatric diagnosis, were aged 65 or older, had skin cancer or were not being discussed in a tumour board. The final validated model was optimised to maximise sensitivity at 85.9% and achieved an area under the curve (AUC) of 0.75, a balanced accuracy of 68.5% and specificity of 51.2%. Conclusion: Beyond conventional screening tools, results might contribute to identify patients at risk to be neglected in terms of referral to psycho-oncology within routine oncological care. Keywords: cancer; machine learning; mental disorders; psycho-oncology; psychological support

Burnout among Male Physicians: A Controlled Study on Pathological Personality Traits and Facets

There is a high prevalence of job burnout in physicians, impacting both the professional and personal levels. This study aimed to investigate whether physicians with burnout show specific pathological traits and facets of their personalities compared with healthy controls, according to the dimensional personality models in the ICD-11 and DSM-5. The role of perceived stress, anxiety, and depression were exploratively investigated regarding group differences. Male physicians (n = 60) were recruited into two groups (burnout vs. healthy). The Personality Inventory for the DSM-5 Brief Form Plus (PID5BF+) and the Maslach Burnout Inventory (MBI) were applied. The Wilcoxon rank-sum test (WRS) showed group differences in five of the six traits and in six of the seventeen facets of the PID5BF+. Multiple binary logistic regression, controlling for age, showed that deceitfulness (3.34 (1.36–9.35), p = 0.013) and impulsivity (10.20 (2.4–61.46), p = 0.004) significantly predicted burnout. Moreover, the WRS showed significant group differences in perceived stress, depressive, and anxiety symptoms (all p < 0.00)]. The findings suggest a relationship between pathological personality facets and burnout in a sample of male physicians. In particular, the facets of deceitfulness and impulsivity appear to play an important role. Furthermore, burnout showed well-known associations with perceived stress, depressive, and anxiety symptoms

Reading Wishes from the Lips: Cancer Patients’ Need for Psycho-Oncological Support during Inpatient and Outpatient Treatment

Background: Psycho-oncological support (PO) is an effective measure to reduce distress and improve the quality of life in patients with cancer. Currently, there are only a few studies investigating the (expressed) wish for PO. The aim of this study was to evaluate the number of patients who request PO and to identify predictors for the wish for PO. Methods: Data from 3063 cancer patients who had been diagnosed and treated at a Comprehensive Cancer Center between 2011 and 2019 were analyzed retrospectively. Potential predictors for the wish for PO were identified using logistic regression. As a novelty, a Back Propagation Neural Network (BPNN) was applied to establish a prediction model for the wish for PO. Results: In total, 1752 patients (57.19%) had a distress score above the cut-off and 14.59% expressed the wish for PO. Patients’ requests for pastoral care (OR = 13.1) and social services support (OR = 5.4) were the strongest predictors of the wish for PO. Patients of the female sex or who had a current psychiatric diagnosis, opioid treatment and malignant neoplasms of the skin and the hematopoietic system also predicted the wish for PO, while malignant neoplasms of digestive organs and older age negatively predicted the wish for PO. These nine significant predictors were used as input variables for the BPNN model. BPNN computations indicated that a three-layer network with eight neurons in the hidden layer is the most precise prediction model. Discussion: Our results suggest that the identification of predictors for the wish for PO might foster PO referrals and help cancer patients reduce barriers to expressing their wish for PO. Furthermore, the final BPNN prediction model demonstrates a high level of discrimination and might be easily implemented in the hospital information system