■了解肌肉骨骼疼痛的原因和机制对于开发有效的治疗方法和改善患者预后至关重要。自我报告措施,如疼痛绘图比例尺,涉及个人对他们的疼痛程度进行评分。在这项技术中,个人在他们经历疼痛的区域涂色,并且基于所描绘的疼痛强度对所得到的图片进行评级。分析疼痛绘图(PD)通常涉及测量疼痛区域的大小。有几项研究专注于评估PD的临床使用,现在,随着数字PD的引入,这些平台的可用性和可靠性需要验证。传统和数字PD之间的比较研究显示出良好的一致性和可靠性。过去20年来,PD收购的演变反映了数字技术的商业化。然而,笔在纸上的方法似乎更被患者接受,但是目前没有用于扫描PD的标准化方法。
■这项研究的目的是评估使用各种数字扫描仪通过网络平台进行的PD分析的准确性。主要目标是证明简单且负担得起的移动设备可用于获取PD而不会丢失重要信息。
■生成了两组PD:一组增加了216个彩色圆圈,另一组由在成年男性的正面视图身体图上随机分布的各种红色形状组成。然后将这些图纸以彩色打印在A4纸上,包括角落的QR码,以允许自动对齐,并随后使用不同的设备和应用进行扫描。使用的扫描仪是不同尺寸和价格的平板扫描仪(专业,便携式平板,和家用打印机或扫描仪),不同价格范围的智能手机,和6个虚拟扫描仪应用程序。由相同的操作者在正常光条件下进行采集。
■高饱和度颜色,如红色,青色,洋红色,黄色,被所有设备准确识别。小的百分比误差,中等,所有设备的大痛点始终低于20%,较小的值与较大的区域相关联。此外,误差百分比与斑点大小之间存在显著负相关(R=-0.237;P=.04).所提出的平台被证明是健壮和可靠的,可以通过各种扫描设备获取纸质PD。
■这项研究表明,Web平台可以准确地分析通过各种数字扫描仪获取的PD。研究结果支持使用简单且具有成本效益的移动设备进行PD采集,而不会影响数据质量。使用所提出的平台标准化扫描过程可以有助于在临床和研究环境中更有效和一致的PD分析。
UNASSIGNED: Understanding the causes and mechanisms underlying musculoskeletal pain is crucial for developing effective treatments and improving patient outcomes. Self-report measures, such as the Pain Drawing Scale, involve individuals rating their level of pain on a scale. In this technique, individuals color the area where they experience pain, and the resulting
picture is rated based on the depicted pain intensity. Analyzing pain drawings (PDs) typically involves measuring the size of the pain region. There are several studies focusing on assessing the clinical use of PDs, and now, with the introduction of digital PDs, the usability and reliability of these platforms need validation. Comparative studies between traditional and digital PDs have shown good agreement and reliability. The evolution of PD acquisition over the last 2 decades mirrors the commercialization of digital technologies. However, the pen-on-paper approach seems to be more accepted by patients, but there is currently no standardized method for scanning PDs.
UNASSIGNED: The objective of this study was to evaluate the accuracy of PD analysis performed by a web platform using various digital scanners. The primary goal was to demonstrate that simple and affordable mobile devices can be used to acquire PDs without losing important information.
UNASSIGNED: Two sets of PDs were generated: one with the addition of 216 colored circles and another composed of various red shapes distributed randomly on a frontal view body chart of an adult male. These drawings were then printed in color on A4 sheets, including QR codes at the corners in order to allow automatic alignment, and subsequently scanned using different devices and apps. The scanners used were flatbed scanners of different sizes and prices (professional, portable flatbed, and home printer or scanner), smartphones with varying price ranges, and 6 virtual scanner apps. The acquisitions were made under normal light conditions by the same operator.
UNASSIGNED: High-saturation colors, such as red, cyan, magenta, and yellow, were accurately identified by all devices. The percentage error for small, medium, and large pain spots was consistently below 20% for all devices, with smaller values associated with larger areas. In addition, a significant negative correlation was observed between the percentage of error and spot size (R=-0.237; P=.04). The proposed platform proved to be robust and reliable for acquiring paper PDs via a wide range of scanning devices.
UNASSIGNED: This study demonstrates that a web platform can accurately analyze PDs acquired through various digital scanners. The findings support the use of simple and cost-effective mobile devices for PD acquisition without compromising the quality of data. Standardizing the scanning process using the proposed platform can contribute to more efficient and consistent PD analysis in clinical and research settings.