OBJECTIVE The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment and thus provided an adjunctive dimension in patient assessment. The clinical interpretability and confounders of the 5R-STS remain poorly understood. In clinical use, it became apparent that 5R-STS performance may differ between patients with lumbar disk herniation (LDH), lumbar spinal stenosis (LSS) with or without low-grade spondylolisthesis, and chronic low back pain (CLBP). We seek to evaluate the extent of diagnostic information contained within 5R-STS testing. METHODS Patients were classified into gold standard diagnostic categories based on history, physical examination, and imaging. Crude and adjusted comparisons of 5R-STS performance were carried out among the three diagnostic categories. Subsequently, a machine learning algorithm was trained to classify patients into the three categories using only 5R-STS test time and patient age, gender, height, and weight. RESULTS From two prospective studies, 262 patients were included. Significant differences in crude and adjusted test times were observed among the three diagnostic categories. At internal validation, classification accuracy was 96.2% (95% CI 87.099.5%). Classification sensitivity was 95.7%, 100%, and 100% for LDH, LSS, and CLBP, respectively. Similarly, classification specificity was 100%, 95.7%, and 100% for the three diagnostic categories. CONCLUSION 5R-STS performance differs according to the etiology of back and leg pain, even after adjustment for demographic covariates. In combination with machine learning algorithms, OFI can be used to infer the etiology of spinal back and leg pain with accuracy comparable to other diagnostic tests used in clinical examination. These slides can be retrieved under Electronic Supplementary Material.

中文翻译：

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基于客观功能测试的腰腿痛的初步分类：将机器学习应用于诊断的初步研究。

目的五重复坐站（5R-STS）测试旨在捕获客观功能障碍，从而为患者评估提供辅助维度。5R-STS的临床解释性和混杂因素仍然知之甚少。在临床使用中，很明显，在伴或不伴有低度腰椎滑脱和慢性下腰痛（CLBP）的腰椎间盘突出症（LDH），腰椎管狭窄（LSS）的患者之间，5R-STS表现可能有所不同。我们寻求评估5R-STS测试中包含的诊断信息的范围。方法根据病史，体格检查和影像学将患者分为金标准诊断类别。在三个诊断类别中对5R-STS性能进行了粗略和调整后的比较。后来，训练了机器学习算法，仅使用5R-STS测试时间以及患者的年龄，性别，身高和体重将患者分为三类。结果从两项前瞻性研究中，纳入262例患者。在三个诊断类别中，观察到粗略和调整后的测试时间存在显着差异。在内部验证时，分类准确性为96.2％（95％CI 87.099.5％）。LDH，LSS和CLBP的分类敏感性分别为95.7％，100％和100％。同样，三个诊断类别的分类特异性分别为100％，95.7％和100％。结论5R-STS的表现根据背部和腿部疼痛的病因而有所不同，即使在调整了人口统计学协变量后也是如此。结合机器学习算法，OFI可用于推断脊柱后腿疼痛的病因，其准确性可与临床检查中使用的其他诊断测试相媲美。这些幻灯片可以在电子补充材料下找到。