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Related Concept Videos

Data Collection by Experiments01:13

Data Collection by Experiments

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.”
An example of the experimental method is a public clinical trial...
Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Data Reporting and Recording01:24

Data Reporting and Recording

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...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Data Validation01:15

Data Validation

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:
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Published on: September 20, 2018

Response to 'Increasing value and reducing waste in data extraction for systematic reviews: tracking data in data

Jens Jap1, Ian J Saldanha2, Bryant T Smith3

  • 1Center of Evidence Synthesis in Health, Brown University School of Public Health, Providence, USA. jens_jap@brown.edu.

Systematic Reviews
|January 26, 2018
PubMed
Summary

Data abstraction in systematic reviews is complex. Descriptive addressing requires specific PDF versions for accurate data tracking, unlike the claim that it is PDF-independent.

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Area of Science:

  • Information Science
  • Systematic Reviews
  • Data Management

Background:

  • Data abstraction is a critical yet time-consuming and error-prone step in systematic reviews.
  • Existing methods for tracking data during abstraction include simple annotation, descriptive addressing, and Cartesian coordinates.
  • The authors Shokraneh and Adams categorize these techniques.

Discussion:

  • This response challenges the assertion that descriptive addressing is PDF-independent.
  • Different PDF versions of the same report can alter the location of text and tables.
  • Therefore, descriptive addressing necessitates referencing a specific PDF version, not just any version of the report.

Key Insights:

  • Descriptive addressing is not PDF-independent; it requires specific PDF version referencing.
  • Version control is crucial for the accuracy of descriptive addressing in data abstraction.
  • Accurate data location tracking is vital for systematic review integrity.

Outlook:

  • Future data abstraction methods should incorporate robust version control mechanisms.
  • Intermediary services like the Data Abstraction Assistant (DAA) can enhance data location traceability.
  • Standardizing source location information will improve systematic review reproducibility.