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Overview of Advanced Functional Groups02:22

Overview of Advanced Functional Groups

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Functional groups are groups of atoms with specific chemical properties that occur within organic molecules and are sometimes denoted as “R”. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.
Types of Advanced Functional Groups
The table below summarizes some of the major functional groups in organic chemistry.
30.4K
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...
1.2K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.6K
Data Reporting and Recording01:24

Data Reporting and Recording

5.5K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Data Validation01:15

Data Validation

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Related Experiment Video

Updated: Feb 16, 2026

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
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TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

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Advancing Towards a CDA-Based Trauma Registry Data Submission.

Martin Staemmler1, Alexander Nischwitz1, Markus Blätzinger2

  • 1Medical Informatics, University of Applied Sciences, Stralsund, Germany.

Studies in Health Technology and Informatics
|January 4, 2018
PubMed
Summary
This summary is machine-generated.

This study assessed trauma registry data for clinical document architecture (CDA) compliance and semantic annotation. 75% of data items were successfully annotated, with challenges arising from missing codes and concepts.

Keywords:
Electronic Health RecordsRegistriesSemantics

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Area of Science:

  • Medical Informatics
  • Health Data Standards
  • Trauma Registry Data Management

Background:

  • Trauma registries are crucial for improving patient care and research.
  • Standardizing data submission enhances interoperability and data quality.
  • Clinical Document Architecture (CDA) offers a framework for structured health information exchange.

Purpose of the Study:

  • To evaluate the feasibility of structuring data from a major trauma registry according to CDA standards.
  • To assess the extent of semantic annotation achievable for trauma registry data.
  • To identify barriers to complete data annotation within the CDA framework.

Main Methods:

  • An assessment was conducted on data submission practices of a large international trauma registry.
  • The study evaluated the degree of compliance with clinical document architecture (CDA) structure.
  • Semantic annotation of data items and values was performed using available coding systems and terminologies.

Main Results:

  • Complete semantic annotation was achieved for 75% of the assessed data items.
  • Annotation failures for the remaining 25% were primarily due to missing codes or concepts.
  • Specific data elements and value sets presented challenges for comprehensive semantic mapping.

Conclusions:

  • Structuring trauma registry data to CDA standards is partially achievable.
  • Gaps in coding systems and terminologies hinder complete semantic annotation.
  • Further development of standardized codes and concepts is necessary for full CDA compliance and enhanced data utilization in trauma registries.