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Kinetic and dynamic models of diving gases in decompression sickness prevention.

机译:预防减压病的潜水气体的动力学和动力学模型。

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摘要

Decompression sickness is a complex phenomenon involving gas exchange, bubble dynamics and tissue response. Relatively simple deterministic compartmental models using empirically derived parameters have been the mainstay of the practice for preventing decompression sickness since the early 1900s. Decades of research have improved our understanding of decompression physiology, and the insights incorporated in decompression models have allowed people to dive deeper into the ocean. However, these efforts have not yet, and are unlikely in the near future, to result in a 'universal' deterministic model that can predict when decompression sickness will occur. Divers using current recreational dive computers need to be aware of their limitations. Probabilistic models based on the estimation of parameters using modern statistical methods from large databases of dives offer a new approach and can provide a means of standardisation of deterministic models. Future improvements in decompression practice will depend on continued improvement in understanding the kinetics and dynamics of gas exchange, bubble evolution and tissue response, and the incorporation of this knowledge in risk models whose parameters can be estimated from large databases of human and animal data.
机译:减压病是一种复杂的现象,涉及气体交换,气泡动力学和组织反应。自1900年代初以来,使用经验导出参数的相对简单的确定性隔室模型一直是预防减压病的主要手段。数十年的研究增进了我们对减压生理学的理解,并且减压模型中包含的见解使人们能够更深入地进入海洋。但是,这些努力尚未完成,并且在不久的将来不太可能形成可预测减压病何时发生的“通用”确定性模型。使用当前休闲潜水计算机的潜水员需要意识到其局限性。使用来自大型潜水数据库的现代统计方法基于参数估计的概率模型提供了一种新方法,并且可以提供一种确定性模型标准化的方法。减压实践的未来改进将取决于对气体交换动力学,动力学,气泡演化和组织反应的理解的不断改进,以及将这种知识纳入风险模型的风险模型,这些风险模型的参数可以从大型人和动物数据数据库中估算出来。

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