Related Experiment Video
Updated: Feb 10, 2026
08:08
Cell-based Assay Protocol for the Prognostic Prediction of Idiopathic Scoliosis Using Cellular Dielectric Spectroscopy
Published on: October 16, 2013
11.0K
Scoring Idiopathic Granulomatous Mastitis: An Effective System for Predicting Recurrence?
Tonguç Utku Yılmaz1, Bora Gürel2, Sertaç Ata Güler1
1Department of General Surgery, Kocaeli University School of Medicine, Kocaeli, Turkey.
European Journal of Breast Health
|May 19, 2018
Summary
Idiopathic granulomatous mastitis recurrence is linked to factors like multiple births and high BMI. A new scoring system helps predict recurrence risk in patients with this breast condition.
Area of Science:
- Breast pathology
- Surgical oncology
- Inflammatory diseases
Background:
- Idiopathic granulomatous mastitis (IGM) lacks a clear cause, definitive classification, or scoring system.
- Understanding recurrence factors is crucial for effective patient management.
Purpose of the Study:
- To identify factors associated with IGM recurrence.
- To develop a scoring system for predicting IGM recurrence risk.
Main Methods:
- Retrospective analysis of 53 IGM patients treated surgically.
- Evaluation of clinical, ultrasonographic, and histopathological factors.
- Comparison between recurrent and non-recurrent cases.
Main Results:
- Recurrence observed in 8 patients.
- Significant factors for recurrence included: >2 births, >18 months lactation, BMI >31, fistula, abscess, and high inflammation score.
- Recurrent cases had a higher severity score (5.1±0.6) vs. non-recurrent (1.9±1.0).
Conclusions:
- The developed "Granulomatous Mastitis Score" aids in predicting recurrence risk.
- Patients exhibiting identified factors are at higher risk for IGM recurrence.
Related Concept Videos
01:06Introduction to z Scores
11.3K
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...
11.3K
01:05Introduction 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
01:17z Scores and Area Under the Curve
19.6K
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...
19.6K
02:27Predicting Molecular Geometry
46.1K
VSEPR Theory for Determination of Electron Pair Geometries
46.1K
01:07z 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
01:03Prediction 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