単一の査読者による文献スクリーニングにおける誤った排除を,二次査読者としてのAIツールを使用して検出する:評価研究のための研究プロトコル
Lisa Affengruber1,2, Jos Kleijnen3, Gerald Gartlehner4,5
1Department for Evidence-based Medicine and Evaluation, Cochrane Austria, University of Continuing Education Krems, Dr. Karl Dorrek Strasse 30, 3500, Krems, Austria. lisa.affengruber@donau-uni.ac.at.
Systematic reviews
|January 4, 2026
まとめ
No abstract available in PubMed .
さらに関連する動画
12:18A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
7.9K
12:30Avidity-based Extracellular Interaction Screening AVEXIS for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions
Published on: March 5, 2012
22.1K
関連する概念動画
Quantifying and Rejecting Outliers: The Grubbs Test
3.4K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.4K
Bias
7.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.2K
