Negative Predictive Value
Negative Predictive Value - Related Videos
02:27
Predicting Molecular Geometry
0 Views •
2020
VSEPR Theory for Determination of Electron Pair Geometries The following procedure uses VSEPR theory to determine the electron pair geometries and the molecular structures: Write the Lewis structure of the molecule or polyatomic ion. Count the number of electron groups (lone pairs and bonds) around the central atom. A single, double, or triple bond counts as one region of electron density. Identify the electron-pair geometry based on the number of electron groups: linear, trigonal planar,...
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
0 Views •
Cited by 79 •
2011
Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.
01:23
Negative Externalities
0 Views •
2025
A negative externality occurs when an economic transaction imposes unintended costs on third parties who are not directly involved in the market transaction. These external costs are not captured in the market price. Negative Externalities and Market Failure Consider a chemical manufacturing plant that produces industrial chemicals for sale. The plant's private costs include raw materials, labor, equipment, and maintenance, which are reflected in the market price of the chemicals. However,...
00:58
Positive, Negative, and Zero Work
0 Views •
2025
Work is done on an object when energy is transferred to the object. In other words, work is done when a force acts on a body that undergoes a displacement from one position to another. By definition, the work done by a force is the integral of the force with respect to the displacement along its path. Forces can vary as a function of position, and displacements can occur along various paths between two points. The magnitude of a force multiplied by the cosine of the angle that the force makes...
06:56
Isolation of Double Negative αβ T Cells from the Kidney
0 Views •
Cited by 26 •
2014
DNabT cells are rare among peripheral T cells; however, they are abundant in certain non-lymphoid tissues. Difficulty of isolating DN T cells from non-lymphoid tissue hinders their functional analysis despite increasing recognized pathophysiologic significance. We describe a novel protocol for isolation of highly purified DN T cells from murine kidney.
FAQs
To calculate negative predictive value, first identify true negatives and false negatives among all negative results, then divide TN by TN + FN. The denominator includes every person or sample receiving a negative result. A high value means false negatives represent a small proportion of negative results, supporting confidence that negative findings correctly exclude the target condition in that population.
Population prevalence changes the mix of affected and unaffected individuals tested. When prevalence differs, the number of affected individuals who may receive false-negative results can also change relative to the total number of negative results. Consequently, negative predictive value is context dependent, and a result established in one population should not automatically be applied unchanged to another.
Sensitivity influences negative predictive value because it affects how many affected cases are missed and classified as false negatives. Holding prevalence and other conditions constant, higher sensitivity generally produces fewer false negatives and can raise the proportion of negative results that are true negatives. This makes sensitivity especially relevant when researchers assess the safety of ruling out a condition.
Negative predictive value combines test performance with the population in which the test is evaluated. Even if the testing procedure remains unchanged, differences in sensitivity or disease prevalence can alter the proportion of negative results that are true negatives. Therefore, interpreting the measure requires attention to the study population and testing conditions rather than treating the value as universally constant.
Researchers can organize test outcomes into true negatives and false negatives, usually within the group receiving negative results. They then apply TN/(TN + FN) and report the resulting proportion or percentage. The calculation should be tied to the defined study population, because the observed value describes how reliably negative findings excluded the condition in that specific evaluation.
The measure is useful when investigators want to compare how confidently different testing strategies exclude a condition. Comparisons are most meaningful when the strategies are evaluated in comparable populations, because prevalence affects the result. In screening, diagnostic, public health, and research settings, the comparison can help identify which approach better supports decisions based on negative findings.