面生长百分位曲线:来自面生长联盟研究研究的临床工具
Heesoo Oh1, Kevin M Middleton2, Manish Valiathan3
1Department of Orthodontics, University of the Pacific, Arthur A Dugoni School of Dentistry, San Francisco, Calif.
概括
新的头脑测量标准为面测量提供了性别特异的百分比增长曲线. 这些工具有助于临床医生评估个体生长模式,并计划个性化治疗.
科学领域:
- 头骨面部的发展
- 矯正牙科 矯正牙科是一種矯正牙科.
- 儿科牙科 儿科牙科
背景情况:
- 为准确的生长评估,开发用于面脑表测量测量标准数据至关重要.
- 现有的脑力测量标准可能不反映北美多样化的人口.
- 纵向数据对于理解增长轨迹至关重要.
研究的目的:
- 建立新的,特定于性别的百分比增长曲线,用于面头脑测量测量.
- 为了更广泛的适用性,将多样化的北美样本纳入.
- 开发一种用于临床的实用工具.
主要方法:
- 利用了来自2100名受试者 (1056名男性,1044名女性) 的纵向数据,并进行了17,290次脑电图 (年龄为2.5至31.3岁).
- 计算了基本,大和下的24个线性脑电测量测量.
- 采用多层非线性增长模型来估计增长里程碑和创建百分位曲线,开发一个Web界面.
主要成果:
- 估计性别特定的成长里程碑,包括年龄和峰值增长速度,使用双重物流模型.
- 在面区域之间显示了峰值生长速度时间的变化.
- 创建和交叉验证面生长百分位曲线,使用功能性网络工具来检索个别百分位.
结论:
- 新的百分点生长曲线和网络工具为评估个体面生长提供了可靠的方法.
- 临床医生可以识别生长偏差,并预测个性化治疗计划的未来潜力.
- 支持加强在正牙科和相关领域的临床决策.
相关概念视频
Survival Curves
931
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
931
Bacterial Growth Curve
5.7K
The bacterial growth curve is a fundamental concept in microbiology that describes the dynamics of bacterial population growth in a closed system with controlled environmental conditions, such as temperature and nutrient availability. This curve is divided into four distinct phases: lag, log (exponential), stationary, and death phases, each reflecting a unique stage of bacterial adaptation and growth. During the lag phase, bacteria acclimate to their surroundings by synthesizing essential...
5.7K
Microbial Growth Measurement: Direct Methods
2.9K
Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
2.9K
Microbial Growth Measurement: Indirect Methods
2.3K
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
2.3K
Growth Models with Integration: Problem Solving
164
In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
164
Exponential Equations for Modeling Growth
459
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
459


