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In the CN group all three measures demonstrated trivial sensitivity to change.
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It is trivial to calculate sensitivity and specificity from such a matrix if desired, and allows for more useful measures of performance as described herein.
The first is whether this reflects false positive findings or unreasonable sensitivity to trivial symptoms by computerized history-taking.
Since the converged tangent stiffness is available from the response analysis, the computing cost for the sensitivity analysis is trivial.
These results are almost paradoxical in light of what is known about random heteropolymers and the sensitivity of their properties to seemingly trivial mutations.
Mathematically elegant methods of variance decomposition and sensitivity clustering of parameters revealed non-trivial biological outcomes.
To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting.
Since already factorized system matrix obtained from the power flow analysis is utilized to evaluate the adjoint response and sensitivity, computational cost for the DSA is trivial.
For example, questionnaires may differ in their sensitivity to identify cases where respondents consider their symptoms trivial.
Writing a good SAP for a model-based sensitivity or ancillary analysis [ 1, 2] involves non-trivial decisions on and justification of many aspects of the chosen model setting.
Within the aMCI and AD groups the AQ demonstrated small sensitivity to change while its sensitivity to change in the CN group was trivial.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com