New Tools to Measure Ejaculatory Latency-Arousal to Ejaculation Time Interval and Erection to Ejaculation Time

Gajanan S Bhat1, Anuradha Shastry2

  • 1Department of Urology, TSS Shripad Hegde Kadave Institute of Medical Sciences, Sirsi and General Hospital, Honavar, Karnataka, India.

Urology
|February 13, 2018
PubMed
Summary

Novel tools, arousal to ejaculation time interval (AETI) and erection to ejaculation time interval (EETI), effectively measure ejaculatory latency across various sexual activities. This pilot study validates their use in assessing normal and premature ejaculation.

Related Concept Videos

Male Sexual Response: Erection & Ejaculation01:17

Male Sexual Response: Erection & Ejaculation

Sexual stimulation can take various forms, such as physical touch and visual or auditory cues. When this happens, the parasympathetic reflex in the sacral portion of the spinal cord is activated. This reflex stimulates the release of nitric oxide (NO), which then dilates the arterioles in the penis, increasing blood flow to the erectile tissues - the corpora cavernosa and corpus spongiosum.
The blood filling the erectile tissues compresses the veins, which helps to prevent blood from leaving...
21.9K
Interval Level of Measurement00:55

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
19.3K
Prediction Intervals01:03

Prediction Intervals

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. 
3.4K
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.8K
Improper Integrals: Infinite Intervals01:29

Improper Integrals: Infinite Intervals

An integral is classified as improper due to an infinite interval when at least one of its limits of integration extends to positive or negative infinity. In such cases, the region under the curve is unbounded, and standard techniques for evaluating definite integrals are not directly applicable. Instead, the improper integral is defined through a limiting process that allows one to determine whether the accumulated area remains finite despite the infinite domain.Application to Exponential...
118
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
10.2K