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相关概念视频

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Synthetic and semisynthetic opioids are pivotal in pain management and tackling opioid addiction. Semisynthetic opioids, including morphinans (morphine derivatives), oxycodone, oxymorphone, hydrocodone, and hydromorphone, have improved pharmacokinetic profiles compared to morphine. Additionally, heroin and 6-MAM (6-Monoacetylmorphine) show better CNS penetration than morphine due to heightened lipid solubility. Hydromorphone, a potent opioid, undergoes hepatic metabolism to form the active...
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Opioids are a class of drugs that mimic endogenous opioid peptides and act on opioid receptors, and help in pain relief. These compounds are classified as natural, synthetic, or semi-synthetic. Natural opioids, like morphine, codeine, and thebaine, are derived from the opium poppy plant (Papaver somniferum or Papaver album) and are termed opiates. Synthetic opioids are artificial, while semi-synthetic opioids combine natural and synthetic compounds. Morphine, a prototypical opioid, possesses a...
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Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...
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预测手术后持续使用阿片类药物:一种机器学习方法

Natalie B Baxter1, Allister Z Ho2, Jacqueline N Byrd3,4

  • 1University of Michigan Medical School.

Plastic and reconstructive surgery
|September 29, 2023
PubMed
概括
此摘要是机器生成的。

机器学习模型可以预测手术后持续使用阿片类药物. 密歇根基因组学倡议模型显示出强大的预测性能,有助于早期干预阿片类药物成预防.

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科学领域:

  • 整形外科手术 整形外科手术
  • 数据科学数据科学数据科学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 持续使用阿片类药物是手术后的一个重大问题.
  • 识别有风险的患者对于预防阿片类药物成至关重要.

研究的目的:

  • 评估用于预测手术患者持续使用阿片类药物的机器学习算法.
  • 为了比较不同机器学习模型的性能.

主要方法:

  • 使用一般手术和手术数据集训练了两个算法 (密歇根基因组学倡议和索赔模型).
  • 模型在2015-2018年手术患者数据 (n=889) 上进行了测试.
  • 模型歧视是用接收器操作曲线 (AUC) 下的面积来衡量的.

主要成果:

  • 密歇根基因组学倡议模型的AUC为0.84 (手术数据) 和0.85 (全队列).
  • 索赔模型的表现较低,AUC为0.69 (手术数据) 和0.52 (阿片类药物未使用队列).
  • 21%的手术患者持续使用阿片类药物.

结论:

  • 机器学习,特别是密歇根基因组学倡议模型,在识别手术后患有持续阿片类药物使用风险的患者方面表现有前途.
  • 早期识别可以促进预防阿片类药物成的干预措施.