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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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One-Compartment Open Model: Urinary Excretion Data and Determination of k01:11

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The one-compartment open model leverages urinary excretion data to estimate renal clearance, which gauges the kidney's capacity to expel a drug. This method offers several benefits, including directly measuring drug elimination and assessing the kidney's contribution to overall drug clearance. However, this approach has limitations. It assumes sole renal excretion of the drug, which is not true for all drugs. Accurate urinary excretion and plasma drug concentration measurement can also...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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How Data are Classified: Numerical Data00:59

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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.
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In vivo Imaging Method to Distinguish Acute and Chronic Inflammation
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What distinguishes data from models?

Sabina Leonelli1

  • 1Exeter Centre for the Study of the Life Sciences (Egenis) & Department of Sociology, Philosophy and Anthropology, University of Exeter, Byrne House, St Germans Road, Exeter, EX4 4PJ UK.

European Journal for Philosophy of Science
|March 16, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework to differentiate the roles of data and models in scientific research. It clarifies how their function, not intrinsic properties, defines their epistemic contribution to empirical inquiry.

Keywords:
Big dataData modelData processingEmpiricismExperimentationInferencePhenomicsPlant scienceResearch practiceStatistics

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Area of Science:

  • Philosophy of Science
  • Scientific Methodology
  • Epistemology

Background:

  • Current characterizations of data models, such as Suppes', are insufficient for distinguishing their epistemic roles.
  • Scientific practice involves complex interactions between data and models that require nuanced understanding.

Purpose of the Study:

  • To propose a framework that explicates and distinguishes the epistemic roles of data and models in empirical inquiry.
  • To offer a new characterization of data models based on their function within scientific practice.

Main Methods:

  • Analysis of scientific practices, focusing on data processing in plant phenotyping.
  • Critique of existing philosophical accounts of data and models (e.g., Suppes).

Main Results:

  • Distinction between practices that make data usable as evidence and those that use data to represent phenomena.
  • Demonstration that the function of objects as data or models depends on their role in investigation, not intrinsic properties.
  • Proposal of a data model characterization that avoids fixed hierarchies or exclusionary definitions.

Conclusions:

  • The epistemic roles of data and models are determined by their practical function in scientific investigation.
  • A more flexible and practice-oriented understanding of data models is needed.
  • This framework offers a robust way to analyze the interplay of data and models in empirical research.