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Related Concept Videos

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Updated: Sep 9, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Twelve tips for data extraction for knowledge syntheses.

Lauren A Maggio1, Joseph A Costello1, Dario M Torre2

  • 1Department of Medical Education, University of Illinois Chicago, Chicago, IL, USA.

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|September 2, 2025
PubMed
Summary
This summary is machine-generated.

This study offers 12 practical tips for data extraction in knowledge syntheses, a crucial step in medical education research. These guidelines enhance the rigor and efficiency of synthesizing evidence for better educational practice and policy.

Keywords:
data extractionknowledge synthesisliterature reviews

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Area of Science:

  • Medical Education Research
  • Evidence Synthesis Methodologies

Background:

  • Knowledge syntheses are increasingly influential in medical education practice, research, and policy.
  • Existing guidance for knowledge synthesis often lacks practical detail on data extraction.
  • Flawed or time-consuming data extraction can compromise the quality of evidence synthesis.

Purpose of the Study:

  • To address the gap in practical guidance for data extraction in knowledge syntheses.
  • To provide actionable strategies for improving the quality and efficiency of data extraction.
  • To offer 12 evidence-based tips for conducting systematic data extraction.

Main Methods:

  • The article presents 12 practical tips for data extraction, grounded in literature and expert experience.
  • Tips are organized into creating a data extraction tool and operationalizing its use.
  • Guidance covers aligning extraction with objectives, team-based approaches, discrepancy resolution, and piloting.

Main Results:

  • The 12 tips offer concrete strategies for systematic data extraction.
  • The proposed methods aim to enhance the rigor, efficiency, and reliability of knowledge syntheses.
  • Practical advice is provided for tool development and implementation in team settings.

Conclusions:

  • Effective data extraction is critical for high-quality knowledge syntheses in medical education.
  • The provided tips offer a practical framework to improve data extraction processes.
  • Implementing these strategies can lead to more reliable and efficient evidence synthesis for educational advancement.