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生命体征测量存在尾数偏差和边界效应。

Vital sign measurements demonstrate terminal digit bias and boundary effects.

机构信息

Department of Neurology, Department of Neurosurgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia.

School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.

出版信息

Emerg Med Australas. 2024 Aug;36(4):543-546. doi: 10.1111/1742-6723.14395. Epub 2024 Feb 27.

Abstract

OBJECTIVE

The measurement and recording of vital signs may be impacted by biases, including preferences for even and round numbers. However, other biases, such as variation due to defined numerical boundaries (also known as boundary effects), may be present in vital signs data and have not yet been investigated in a medical setting. We aimed to assess vital signs data for such biases. These parameters are clinically significant as they influence care escalation.

METHODS

Vital signs data (heart rate, respiratory rate, oxygen saturation and systolic blood pressure) were collected from a tertiary hospital electronic medical record over a 2-year period. These data were analysed using polynomial regression with additional terms to assess for underreporting of out-of-range observations and overreporting numbers with terminal digits of 0 (round numbers), 2 (even numbers) and 5.

RESULTS

It was found that heart rate, oxygen saturation and systolic blood pressure demonstrated 'boundary effects', with values inside the 'normal' range disproportionately more likely to be recorded. Even number bias was observed in systolic heart rate, respiratory rate and blood pressure. Preference for multiples of 5 was observed for heart rate and blood pressure. Independent overrepresentation of multiples of 10 was demonstrated in heart rate data.

CONCLUSION

Although often considered objective, vital signs data are affected by bias. These biases may impact the care patients receive. Additionally, it may have implications for creating and training machine learning models that utilise vital signs data.

摘要

目的

生命体征的测量和记录可能会受到偏好偶数和整数等偏差的影响。然而,在生命体征数据中可能还存在其他偏差,例如由于定义的数值边界(也称为边界效应)而导致的偏差,但尚未在医学环境中进行研究。我们旨在评估生命体征数据是否存在此类偏差。这些参数在临床上很重要,因为它们会影响护理升级。

方法

在 2 年的时间内,从一家三级医院的电子病历中收集生命体征数据(心率、呼吸频率、血氧饱和度和收缩压)。使用多项式回归分析这些数据,并添加额外的术语来评估超出范围的观察值报告不足和以数字 0(整数)、2(偶数)和 5 结尾的数字报告过多的情况。

结果

发现心率、血氧饱和度和收缩压表现出“边界效应”,“正常”范围内的值更有可能被记录。在收缩压心率、呼吸频率和血压方面观察到偶数偏好。心率和血压数据中观察到对 5 的倍数的偏好。在心率数据中还证明了 10 的倍数的独立过度表示。

结论

尽管生命体征数据通常被认为是客观的,但它们受到偏差的影响。这些偏差可能会影响患者接受的护理。此外,这可能对创建和培训使用生命体征数据的机器学习模型产生影响。

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