Private practice, Rome, Italy.
Department of Orthodontics and Pediatric Dentistry, School of Dentistry, and Cell and Developmental Biology, School of Medicine, Center for Human Growth and Development, University of Michigan, and Private practice, Ann Arbor, Mich.
Am J Orthod Dentofacial Orthop. 2020 Dec;158(6):856-867. doi: 10.1016/j.ajodo.2019.10.018. Epub 2020 Sep 29.
During the decision-making process, physicians rely on heuristics that consist of simple, useful procedures for solving problems, intuitive shortcuts that produce reliable decisions based on limited information. In clinical situations characterized by a high degree of uncertainty such as those encountered in orthodontics, cognitive biases and judgment errors related to heuristics are not uncommon. This study aimed at promoting trust in the effective interface between the intuitive reasoning of the orthodontic practitioner and the computational heuristics emerging from simple statistical models.
We propose an integrative model based on the interaction between clinical reasoning and 2 computational tools, cluster analysis and fast-and-frugal trees, to extract a structured craniofacial representation of untreated subjects with Class III malocclusion and to forecast the worsening of the malocclusion over time.
Cluster analysis of cephalometric values from 144 growing subjects with Class III malocclusion followed longitudinally (T1: mean age, 10.2 ± 1.9 years; T2: mean age, 13.8 ± 2.7 years) produced 3 morphologic subgroups with predominant sagittal, vertical, and slight maxillomandibular imbalances. Fast-and-frugal trees applied to different subgroups extracted heuristics that improved the prediction of key features associated with adverse craniofacial growth.
Provided that cephalometric values are placed in the appropriate framework, the matching between simple and fast computational approaches and clinical reasoning could help the intuitive logic, perception, and cognitive inferences of orthodontic practitioners on the outcome of patients affected by Class III disharmony, decreasing errors associated with flawed judgments and improving the accuracy of decision making.
在决策过程中，医生依赖于启发式，这些启发式由解决问题的简单、有用的程序组成，是基于有限信息产生可靠决策的直观捷径。在正畸等高度不确定的临床情况下，与启发式相关的认知偏差和判断错误并不少见。本研究旨在促进信任，即在正畸医生的直观推理和简单统计模型产生的计算启发式之间建立有效的接口。
我们提出了一个基于临床推理和 2 个计算工具（聚类分析和快速而节俭树）之间相互作用的综合模型，以提取未经治疗的 III 类错牙合患者的结构化颅面表示，并预测错牙合随时间的恶化。
对 144 名纵向随访的 III 类错牙合生长患者的头影测量值进行聚类分析（T1：平均年龄 10.2±1.9 岁；T2：平均年龄 13.8±2.7 岁），产生了 3 个具有明显矢状、垂直和轻微上下颌骨不平衡的形态亚组。快速而节俭树应用于不同的亚组，提取出有助于预测与不良颅面生长相关的关键特征的启发式。
只要将头影测量值置于适当的框架内，简单而快速的计算方法与临床推理之间的匹配，可以帮助正畸医生根据 III 类不调患者的结果进行直观的逻辑、感知和认知推断，减少判断错误相关的错误，并提高决策的准确性。
