We read with interest the above article by Zavorsky (2025, Respiratory Medicine, doi:10.1016/j.rmed.2024.107836) concerning reference equations for pulmonary function testing. The author compares a Generalized Additive Model for Location, Scale, and Shape (GAMLSS), which is the standard adopted by the Global Lung Function Initiative (GLI), with a segmented linear regression (SLR) model, for pulmonary function variables. The author presents an interesting comparison; however there are some fundamental issues with the approach. We welcome this opportunity for discussion of the issues that it raises. The author's contention is that (1) SLR provides "prediction accuracies on par with GAMLSS"; and (2) the GAMLSS model equations are "complicated and require supplementary spline tables", whereas the SLR is "more straightforward, parsimonious, and accessible to a broader audience". We respectfully disagree with both of these points.

翻译：我们饶有兴趣地阅读了Zavorsky（2025年，《呼吸医学》，doi:10.1016/j.rmed.2024.107836）关于肺功能测试参考方程的上述文章。作者针对肺功能变量，将全球肺功能倡议（GLI）采纳的标准方法——位置、尺度与形状广义可加模型（GAMLSS）与分段线性回归（SLR）模型进行了比较。作者提出了一个有趣的对比，但该方法存在若干根本性问题。我们欢迎借此机会讨论其引发的问题。作者主张：（1）SLR能提供“与GAMLSS相当的预测精度”；（2）GAMLSS模型方程“复杂且需要补充样条表”，而SLR“更直接、简约且易于更广泛受众理解”。我们对此两点均持不同意见。