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Review and Preview

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...

Review and Preview

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...

Accelerators

Accelerators

Accelerators in concrete serve as admixtures to speed up the hardening process, enabling the concrete to achieve early strength faster. Although accelerators do not necessarily impact the time it takes concrete to set, they reduce this time in practice. A common accelerator is calcium chloride, which is particularly useful for hastening early strength development in cold weather or for rapid repair jobs that require quick heat generation after mixing.
The effectiveness of calcium chloride can...

Machines

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...

Random and Systematic Errors

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...

Systematic Sampling Method

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...

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Related Experiment Video

Updated: Feb 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Can machine learning help accelerate article screening for systematic reviews? Yes, when article separability in

Farhan Ali¹, Amanda Swee-Ching Tan¹, Serena Jun-Wei Wang²

¹National Institute of Education, Nanyang Technological University, Singapore, Singapore.

Research Synthesis Methods

|February 2, 2026

Summary

Automated strategies for systematic reviews show varied performance. A new heuristic using cluster separability in machine learning (ML) models can predict when ML screening effectively saves work.

Keywords:

active learning embedding large language models machine learning systematic reviews

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Last Updated: Feb 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Area of Science:

Bibliometrics
Health Informatics
Artificial Intelligence

Background:

Systematic reviews are crucial but labor-intensive due to increasing research publications.
Automated methods are needed to expedite systematic review processes.

Purpose of the Study:

To evaluate the effectiveness of various machine learning (ML) models and large language models (LLMs) for automating systematic review screening.
To identify predictors for successful ML-assisted screening.

Main Methods:

Tested classical and deep learning ML models on education systematic review datasets.
Assessed prompt engineering with GPT-3.5 and GPT-4 LLMs for few-shot learning.
Investigated cluster separability in high-dimensional embedding space as a predictor.

Main Results:

Performance of ML and LLM models varied significantly across datasets, with work saved ranging from 1.2% to 75.6% at 95% recall.
Cluster separability strongly predicted ML screening utility (R = 0.81) across different models and datasets.

Conclusions:

Machine learning screening performance is highly variable.
The proposed cluster separability heuristic offers a generalizable method to predict and potentially optimize ML-assisted screening pipelines.