Anesthesia and analgesia
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Anesthesia and analgesia · Dec 2022
Improving Preclinical Development of Novel Interventions to Treat Pain: Insanity Is Doing the Same Thing Over and Over and Expecting Different Results.
Preclinical pain research has applied state-of-the-art methods over the past 40 years to describe, characterize, and image molecules, cells, and circuits in rodents to understand the pathophysiology of chronic pain. Despite generating a plethora of novel analgesic targets, pharmaceuticals for chronic pain treatment remain largely limited to the same 6 drug classes as present 40 years ago. It is possible that 40 years of effort has brought us to the verge of a paradigm shift and an explosion of novel analgesic drug classes with remarkable safety, efficacy, and tolerability. ⋯ A path forward is provided by the evolution of clinical research beginning 50 years ago that resulted in methods to reduce bias and enhance transparency and ethics of reporting, moving from case reports to randomized controlled trials to innovative study designs with a focus on rigor, generalizability, and reproducibility. We argue that culture changed in clinical science in part because powerful forces outside the peer review system, especially from federal regulators that approve new drugs and human studies committees that addressed ethical failures of earlier research, mandated change in studies within their purview. Whether an external force will affect change in peclinical pain research is unclear.
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Anesthesia and analgesia · Dec 2022
Identification of Preanesthetic History Elements by a Natural Language Processing Engine.
Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured text data. We describe the utilization of a clinical NLP pipeline intended to identify elements relevant to preoperative medical history by analyzing clinical notes. We hypothesize that the NLP pipeline would identify a significant portion of pertinent history captured by a perioperative provider. ⋯ In this proof-of-concept study, we demonstrated that utilization of NLP produced an output that identified medical conditions relevant to preanesthetic evaluation from unstructured free-text input. Automation of risk stratification tools may provide clinical decision support or recommend additional preoperative testing or evaluation. Future studies are needed to integrate these tools into clinical workflows and validate its efficacy.