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Related Concept Videos

Data Collection by Experiments01:13

Data Collection by Experiments

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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.”
An example of the experimental method is a public...
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Data Reporting and Recording01:24

Data Reporting and Recording

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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 Collection I01:30

Data Collection I

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

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

Systematic Error: Methodological and Sampling Errors

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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...
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Related Experiment Video

Updated: Feb 25, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Increasing value and reducing waste in data extraction for systematic reviews: tracking data in data extraction

Farhad Shokraneh1,2, Clive E Adams3

  • 1Cochrane Schizophrenia Group, The Institute of Mental Health, A Partnership Between The University of Nottingham and Nottinghamshire Healthcare NHS Trust, Nottingham, UK. Farhad.Shokraneh@nottingham.ac.uk.

Systematic Reviews
|August 6, 2017
PubMed
Summary
This summary is machine-generated.

Systematic reviews involve time-consuming data extraction. This paper presents three methods for reporting data locations in full-text reports to improve accuracy and data sharing.

Keywords:
Data extractionData locationIncreasing valuePortable Document Format (PDF)Reducing wasteSystematic reviewsTraceable data

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Area of Science:

  • Information Science
  • Medical Informatics
  • Research Methodology

Background:

  • Data extraction is a critical yet labor-intensive component of systematic reviews.
  • Current methods often involve manual data entry onto forms, posing challenges for validation and data reuse.
  • Linking extracted data back to its original source in full-text reports can be problematic.

Discussion:

  • This paper evaluates three distinct methodologies for documenting data provenance within full-text articles.
  • Each method's strengths and weaknesses regarding accuracy, efficiency, and reusability are compared.
  • The focus is on practical approaches to enhance the reliability of systematic review data.

Key Insights:

  • Improved data location reporting enhances the quality and accuracy of systematic review extraction.
  • Standardized methods facilitate data sharing and re-cycling among researchers, promoting collaborative science.
  • Addressing the challenge of linking data to its source is crucial for robust evidence synthesis.

Outlook:

  • Future research should focus on developing automated or semi-automated tools for data extraction and location tracking.
  • The adoption of standardized reporting methods can streamline systematic review processes globally.
  • Enhanced data provenance will support more dynamic and up-to-date evidence syntheses.