Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A meta-analysis of the long-term effects of antihypertensive therapy on the risk of major cardiovascular disease across 51 randomized trials.

Nature medicine·2026
Same author

Blood pressure parameters and cognitive decline and dementia after stroke or transient ischemic attack: results from the PROGRESS trial.

American journal of hypertension·2026
Same author

Blood pressure lowering for the prevention of REcurrent stroke and Cardiovascular outcomes After acute intracerebral haemorrhage: protocol for an individual Participant data meta-analysis of randomised controlled trials (RECAP-ICH).

Cerebrovascular diseases (Basel, Switzerland)·2026
Same author

Impact of Socioeconomic Status on Functional Outcome After Stroke in Ulaanbaatar, Mongolia.

Journal of the American Heart Association·2026
Same author

Optimal Definition of Early Neurological Deterioration in Thrombolysis-Treated Acute Ischemic Stroke: ENCHANTED Study.

Cerebrovascular diseases (Basel, Switzerland)·2026
Same author

Effect of intensive blood pressure and blood glucose control on cardiovascular outcomes driven by reductions in cardiovascular death and nephropathy: Win ratio analysis of ADVANCE Trial.

European heart journal. Cardiovascular pharmacotherapy·2026

Related Experiment Video

Updated: May 24, 2026

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

Sampling time error in EuroSCORE II.

Michael Poullis1, Brian Fabri, Mark Pullan

  • 1Department of Cardiothoracic Surgery, Liverpool Heart and Chest Hospital, Liverpool, UK. mike.poullis@lhch.nhs.uk

Interactive Cardiovascular and Thoracic Surgery
|February 22, 2012
PubMed
Summary
This summary is machine-generated.

Seasonal variations in cardiac surgery mortality exist. The EuroSCORE II accrual period (May-July 2010) showed a different mortality rate, potentially biasing predictions and reducing the accuracy of this new cardiac surgery risk model.

More Related Videos

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task
05:04

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

Published on: September 21, 2017

Related Experiment Videos

Last Updated: May 24, 2026

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task
05:04

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

Published on: September 21, 2017

Area of Science:

  • Cardiology
  • Medical Statistics

Background:

  • Seasonal variations in mortality rates following cardiac surgery are recognized.
  • The EuroSCORE II, a risk prediction model, utilized data accrued over a specific 12-week period.

Purpose of the Study:

  • To investigate potential biases in the EuroSCORE II model.
  • To determine if the specific data accrual period (May-July 2010) influenced mortality rates compared to the rest of the year.

Main Methods:

  • Analysis of a large study population (18,706 patients).
  • Comparison of mortality rates during the EuroSCORE II accrual period versus other times.

Main Results:

  • A statistically significant difference in mortality rates was observed between the EuroSCORE II accrual period and the remainder of the year.
  • The findings suggest the accrual period may introduce bias into the model's predictions.

Conclusions:

  • The specific 12-week accrual period for EuroSCORE II may impact its predictive accuracy.
  • Further validation is needed to account for potential seasonal biases in cardiac surgery risk models.