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Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.3K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

1.1K
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
1.1K
What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.6K
Metallic Solids02:37

Metallic Solids

20.8K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
20.8K
Structures of Solids02:22

Structures of Solids

18.0K
Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
18.0K
Variation01:19

Variation

8.0K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
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Related Experiment Video

Updated: Feb 8, 2026

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

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Interobserver Variation in Response Evaluation Criteria in Solid Tumors 1.1.

Arunabha Karmakar1, Apeksha Kumtakar1, Himanshu Sehgal1

  • 1Image Core Labs division, Teleradiology Solutions Pvt. Ltd., Plot # 7G, Opp Graphite India Whitefield, Bengaluru, Karnataka 560048, India.

Academic Radiology
|June 24, 2018
PubMed
Summary
This summary is machine-generated.

Radiologists showed moderate agreement using Response Evaluation Criteria in Solid Tumors (RECIST 1.1) for cancer trials. Developing checklists can improve consistency and reduce interpretation errors in imaging response evaluation.

Keywords:
Cancer imagingClinical trialsRECIST 1.1

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Author Spotlight: Enhancing Nuclei Isolation for Multiome Sequencing in Challenging Tumor Microenvironments
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Area of Science:

  • Oncology
  • Radiology
  • Clinical Trials

Background:

  • The Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is the standard for assessing cancer treatment efficacy in clinical trials.
  • Interobserver variability in applying RECIST 1.1 can impact trial outcomes.
  • Evaluating consistency between radiologists is crucial for reliable data.

Purpose of the Study:

  • To assess the consistency of RECIST 1.1 application between two radiologists.
  • To identify reasons for discrepancies in RECIST 1.1 interpretation.
  • To propose methods for reconciling differences and improving future study reliability.

Main Methods:

  • Retrospective review of imaging case report forms from a breast cancer trial.
  • Utilized Cohen's kappa to measure interobserver agreement for target, nontarget, new lesion, and overall response.
  • Senior radiologist reassessed variations to determine causes and suggest improvements.

Main Results:

  • Moderate agreement was observed for target response (κ=0.477), nontarget response (κ=0.578), and overall response (κ=0.510).
  • Percent agreement for overall response was 74.39%, with 25.6% variation.
  • Primary sources of variation included differences in RECIST 1.1 interpretation and image analysis.

Conclusions:

  • Qualitative interpretation differences contribute to interobserver variability despite moderate agreement.
  • Standardized protocols like adjudication and checklists can reduce inconsistencies.
  • Checklists may enhance interobserver agreement and improve the quality of imaging data in clinical trials.