Collaborative on Countering the US Opioid Epidemic [8] has been focusing on comprehensive and collaborative efforts to fundamentally address the opioid epidemic crisis. All of these major initiatives emphasize pain education as a key component in the fight against the dual crises of chronic pain and the opioid epidemic. I am honored to represent the AAPM on the HHS Pain Management Task Force and the NAM Action Collaborative and contribute to these important initiatives of our nation on your behalf.

Blind outcome assessment was considered achievable in this trial. Specific trial protocols enabled blinding beliefs to be reported and instances of unblinding to be described.

This study explored sensitivity to passage organization and its importance in governing the ease of learning a passage in normally achieving and learning disabled children. The results indicated that normally achieving children were more aware of passage organization than learning disabled children. However, both groups focused on dimensions such as sentence length, decoding and vocabulary difficulty and informational load, rather than passage organization as determinants of task difficulty. Moreover, unlike normally achieving children, learning disabled children had substantial difficulties in reorganizing a disorganized passage. Through subsequent training, they learned to sort disorganized sentences into coherent clusters around respective subtopics, and appeared to understand what constitutes an organized paragraph within a passage.

Transfer learning is a standard technique to improve performance on tasks with limited data. However, for medical imaging, the value of transfer learning is less clear [38]. This is likely due to the large domain mismatch between the usual natural-image pre-training (e.g. ImageNet) and medical images. However, recent advances in transfer learning have shown substantial improvements from scale. We investigate whether modern methods can change the fortune of transfer learning for medical imaging. For this, we study the class of large-scale pre-trained networks presented by Kolesnikov et al. [23] on three diverse imaging tasks: chest radiography, mammography, and dermatology. We study both transfer performance and critical properties for the deployment in the medical domain, including: out-of-distribution generalization, data-efficiency, subgroup fairness, and uncertainty estimation. Interestingly, we find that for some of these properties, transfer from natural to medical images is indeed extremely effective, but only when performed at sufficient scale.

Patients with similar characteristics presenting to different hospitals with acute gallbladder pathology do not receive comparable care.