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Choice of legal structures is a key decision that social enterprises make early in their lives. The range of options now includes not only the traditional for-profit and non-profit structures, but also the new hybrid structures. Viewing legal structures primarily as 'governance mechanisms to support the mission', the current social enterprise literature regards 'mission' as the normative basis for legal structure choices. Empirical work in the non-profit and social enterprise literatures, however, surfaces another salient, yet under-theorised concern driving legal structure choices, namely resources. In this paper, we aim to develop resource dependence perspectives as an alternate theoretical lens to understand legal structure choices. In this study of 14 New York based socio-tech enterprises, we uncover how, in an interplay of resource needs, autonomy and legitimacy concerns, legal structures emerge as strategic tools to attract the external resource providers that the social enterprises want to form resource relations with and avoid the ones they are wary of. Our findings contribute to advancing the notion of legal structures as a 'vehicle for resource mobilisation', and to lay the foundations for a resource dependent framework to examine social enterprise legal structure choices. [ABSTRACT FROM AUTHOR]

Objectives: Intracranial hemorrhage (ICH) is a prevalent and potentially fatal consequence of traumatic brain injury (TBI). Timely identification of ICH is crucial to ensure timely intervention and to optimize better patient outcomes. However, the current methods for diagnosing ICH from head computed tomography (CT) scans require skilled personnel (Radiologists and/or Neurosurgeons) who may be unavailable in all centers, especially in rural areas. The aim of this study is to develop a neurotrauma screening tool for identifying ICH from head CT scans of TBI patients. Materials and Methods: We prospectively collected head CT scans from the Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi. Approximately 738 consecutive head CT scans from patients enrolled in the department were collected for this study spanning a duration of 9 months, that is, January 2020 to September 2020. The metadata collected along with the head CT scans consisted of demographic and clinical details and the radiologist's report which was used as the gold standard. A deep learning-based 3D convolutional neural network (CNN) model was trained on the dataset. The pre-processing, hyperparameters, and augmentation were common for training the 3D CNN model whereas the training modules were set differently. The model was trained along with the save best model option and was monitored by validation metrics. The Institute Ethics Committee permission was taken before starting the study. Results: We developed a 3D CNN model for automatically detecting the ICH from head CT scans. The screening tool was tested in 20 cases and trained on 200 head CT scans, with 99 normal head CT and 101 CT scans with some type of ICH. The final model performed with 90% sensitivity, 70% specificity, and 80% accuracy. Conclusion: Our study reveals that the automated screening tool exhibits a commendable level of accuracy and sensitivity in detecting ICH from the head CT scans. The results indicate that the 3D CNN approach has a potential for further exploring the TBI-related pathologies. [ABSTRACT FROM AUTHOR]