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Basic Equation for Pressure Field01:13

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The basic equation for a pressure field in fluid mechanics captures the balance of forces within any segment of fluid, providing a foundational understanding of how pressure changes within fluids under various forces. Generally, two main types of forces act on any part of a fluid: surface forces and body forces. Surface forces arise from pressure differences across points within the fluid, which result in net forces that can vary depending on the local pressure gradient. Body forces, on the...
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Chemical equations represent the identities and relative quantities of substances involved in a chemical reaction. The substances undergoing reaction are called reactants, and their formulas are placed on the left side of the equation. The substances generated by the reaction are called products, and their formulas are placed on the right side of the equation. Plus signs (+) separate individual reactant and product formulas, and an arrow (→) separates the reactant and product (left and right)...
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The equilibrium between a liquid and its vapor depends on the temperature of the system; a rise in temperature causes a corresponding rise in the vapor pressure of its liquid. The Clausius-Clapeyron equation gives the quantitative relation between a substance’s vapor pressure (P) and its temperature (T); it predicts the rate at which vapor pressure increases per unit increase in temperature.
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機能的一般化推定方程式モデルによる緑内障性視野進行の検出

Sanghun Jeong1, Hwayeong Kim2, Sangwoo Moon3

  • 1Department of Statistics, Pusan National University, Busan 46241, Republic of Korea.

International journal of ophthalmology
|January 23, 2026
PubMed
まとめ
この要約は機械生成です。

新しい機能的一般化推定方程式（GEE）モデルは、原発開放隅角緑内障（POAG）患者における緑内障性視野進行を効果的に検出します。この方法は、既存のアルゴリズムよりも多くの症例をより速く特定します。

キーワード:
機能的一般化推定方程式モデル周辺視野進行原発開放隅角緑内障

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科学分野:

  • 眼科学
  • 生物統計学
  • 医療技術

背景:

  • 緑内障性視野進行は、疾患進行の重要な指標です。
  • 進行の早期かつ正確な検出は、適時の介入にとって非常に重要です。
  • 視野進行を検出するための現在の方法には、感度と速度の限界があります。

研究 の 目的:

  • 緑内障性視野進行を検出するための機能的一般化推定方程式（GEE）モデルを開発および検証すること。
  • 提案された機能的GEEモデルのパフォーマンスを確立されたアルゴリズムと比較すること。

主な方法:

  • 十分な視野検査データと追跡調査を行った716人の原発開放隅角緑内障（POAG）患者の716眼のコホートを分析しました。
  • 機能的GEEモデルを501眼でトレーニングし、215眼でテストしました。
  • 平均デビエーション（MD）および視野指数（VFI）率、Advanced Glaucoma Intervention Study（AGIS）およびCollaborative Initial Glaucoma Treatment Study（CIGTS）スコア、および点ごとの線形回帰（PLR）と比較してパフォーマンスを評価しました。

主要な成果:

  • 機能的GEEモデルは、周辺視野進行を有する眼の最も高い割合（54.4％）を特定し、VFI率（34.4％）、PLR（23.3％）、MD率（21.4％）、CIGTS（7.9％）、およびAGIS（5.1％）を大幅に上回りました。
  • 機能的GEEモデルを使用した進行の検出は、他の方法と比較して有意に速かった（調整後P≤0.019）。
  • 機能的GEEモデルとVFI率の間には中程度の合意が観察されました（κ=0.47）。

結論:

  • 機能的GEEモデルは、POAG患者における周辺視野進行の検出において優れたパフォーマンスを示します。
  • この新しいアプローチは、進行検出までの時間を短縮し、患者管理を改善する可能性があります。
  • 機能的GEEモデルは、緑内障性視野変化の分析における有望な進歩を表しています。