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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Appropriate sampling methods ensure 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.
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Hazard Ratio01:12

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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE.

Ahmed A Al-Jaishi1, Monica Taljaard2, Melissa D Al-Jaishi3

  • 1Lawson Health Research Institute, 800 Commissioners Rd E, London, ON, Canada. Ahmed.AlJaishi@lhsc.on.ca.

Systematic Reviews
|October 26, 2022
PubMed
Summary

Machine learning algorithms accurately identify cluster randomized trials (CRTs) from citations. This improves retrieval of important CRT research for better evidence synthesis.

Keywords:
Bibliographic databasesCluster randomized controlled trialMachine learningPredictionSensitivity and specificity

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Area of Science:

  • Bibliometrics and scientometrics
  • Machine learning in health research
  • Clinical trial methodology

Background:

  • Cluster randomized trials (CRTs) are crucial but often misidentified in databases.
  • Difficulty in retrieving CRT reports hinders evidence synthesis and research.
  • Machine learning offers a potential solution for accurate CRT identification.

Purpose of the Study:

  • To develop and validate machine learning algorithms for identifying CRT reports from citations.
  • To improve the discoverability and retrieval of cluster randomized trial literature.

Main Methods:

  • Trained and validated convolutional neural networks and a support vector machine (SVM).
  • Utilized citation information (title, abstract, keywords, subject headings) for prediction.
  • Evaluated algorithms using area under the receiver operating characteristic (AUC) curves.

Main Results:

  • Ensemble algorithm achieved high performance in internal validation (AUC 98.6%) and external validation (AUC 97.8%).
  • Algorithms demonstrated high sensitivity (97.7% internal, 97.6% external) for CRT identification.
  • Specificity was moderately high (85.0% internal, 78.2% external).

Conclusions:

  • Successfully developed high-performance machine learning algorithms to identify CRT reports.
  • The developed algorithms offer high sensitivity and moderate specificity for practical application.
  • Open-source software is provided to facilitate the use of these algorithms.