可以使用M-score来预测IGM患者的复发吗?
Mehmet Akif Ötegeçeli1, Belkıs Nihan Coşkun2, Burcu Yağız2
1Department of Internal Medicine, Gaziantep City Hospital, Gaziantep, Turkey. drakif.93@gmail.com.
European journal of medical research
|February 14, 2026
概括
具有较高的初始M分数在异常性粒状乳头炎 (IGM) 表明疾病复发的风险更大. 对于高M分数的IGM患者来说,可能需要早期的积极治疗,以改善结果.
科学领域:
- 乳腺疾病研究研究.
- 炎症条件 炎症条件
- 医学诊断 医学诊断 医学诊断
背景情况:
- 异常性粒状乳腺炎 (IGM) 是一种经常性炎症性乳腺疾病,影响生育年龄的女性.
- M-score是一个客观测量IGM症状严重程度和治疗反应的工具.
研究的目的:
- 为了研究最初的M-score和病情复发之间的相关性,在Idiopathic Granulomatous Mastitis患者.
- 为了确定初始的M分数是否可以预测IGM复发的可能性.
主要方法:
- 从90名IGM患者的临床和组织病理学数据的回顾性分析.
- 利用M-score客观地评估诊断时的症状严重程度.
- 基于初始M-score值和的存在,比较疾病复发率.
主要成果:
- 初始M评分≥5的患者的复发率 (98.4%) 与M评分<5 (24%) 的患者相比显著更高.
- 的存在与更高的复发率 (93.18%) 与没有 (60.87%) 相比.
- 在接受手术的患者中,没有观察到复发率的显著差异 (p=0.518).
结论:
- M-score有效量化IGM症状严重程度,并可以预测复发风险.
- 一个初始的M分数为5或更高表明需要更积极的治疗策略超越观察或单一治疗.
- 像M-score这样的客观评分系统对于量身定制IGM治疗和管理患者结果至关重要.
更多相关视频
04:05Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
Published on: June 30, 2023
2.9K
07:42Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
Published on: February 7, 2021
5.9K
相关概念视频
Introduction to z Scores
11.4K
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
z scores...
11.4K
Introduction to z Scores
1.4K
A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
z scores...
1.4K
z Scores and Area Under the Curve
19.7K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
19.7K
Predicting Molecular Geometry
46.2K
VSEPR Theory for Determination of Electron Pair Geometries
46.2K
z Scores and Unusual Values
11.1K
The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
11.1K
Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K
