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Related Concept Videos

P-value01:10

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

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Related Experiment Video

Updated: Jun 12, 2026

Precision Implementation of Minimal Erythema Dose (MED) Testing to Assess Individual Variation in Human Inflammatory Response
06:31

Precision Implementation of Minimal Erythema Dose (MED) Testing to Assess Individual Variation in Human Inflammatory Response

Published on: October 3, 2019

Measurement of a possible patch-testing outcome indicator.

Rosella Gallo1, Manuela Baldari, Valentina Fausti

  • 1Section of Dermatology, Di.S.E.M., University of Genoa, Genoa, Italy. rs.gallo@unige.it

Contact Dermatitis
|June 23, 2010
PubMed
Summary
This summary is machine-generated.

A new outcome indicator for patch testing was developed. It measures the percentage of patients with allergic contact dermatitis who improve after avoiding identified allergens, showing 85.2% success.

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Measuring Psoriasis Severity at Home
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Last Updated: Jun 12, 2026

Precision Implementation of Minimal Erythema Dose (MED) Testing to Assess Individual Variation in Human Inflammatory Response
06:31

Precision Implementation of Minimal Erythema Dose (MED) Testing to Assess Individual Variation in Human Inflammatory Response

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Measuring Psoriasis Severity at Home
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Measuring Psoriasis Severity at Home

Published on: March 1, 2024

Area of Science:

  • Dermatology
  • Allergology
  • Clinical Trials

Background:

  • Clinical performance measurements utilize outcome indicators to assess healthcare quality.
  • Developing specific outcome indicators is crucial for evaluating diagnostic procedures like patch testing.

Purpose of the Study:

  • To develop and validate an outcome indicator for patch testing.
  • To measure the clinical improvement rate in patients with allergic or photo-allergic contact dermatitis following allergen identification.

Main Methods:

  • Patients with relevant positive patch/photo-patch test reactions were interviewed 2 months post-testing.
  • Clinical outcomes were assessed based on adherence to recommended allergen avoidance strategies.

Main Results:

  • Over 4 years, 1397/2857 patients had positive reactions; relevance was established in 578.
  • Of 506 interviewed patients, 431 (85.2%) reported clinical improvement after allergen avoidance.
  • Reasons for lack of improvement included non-avoidance (41), other skin conditions (17), or no response to avoidance (17).

Conclusions:

  • The ratio of clinically improved patients after allergen avoidance serves as a valuable outcome indicator for patch testing.
  • This indicator effectively quantifies the success of patch testing in managing allergic contact dermatitis.