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This is the same interpretation given to connecting vertices (of opposite orientation) in a (colored) n-dimensional GFT.
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Although seemingly in contrast to one another and to the perioperative literature, we believe that the overall message is the same once interpretation is taken in context.
Next consider Hector's coreferential interpretation of him as referring to Speedy: The representation of the first proposition is the same as in "Indexical interpretation of him ".
The "best guess" leaves some space for free interpretation, however as long as this interpretation is the same each time a patient fills out the questionnaire there is no devaluation of the test.
The interpretation of the odds ratio for patient-level risk factors (eβ) in ordinary logistic and marginal models is the same, but differs from the interpretation in the multilevel logistic model [ 29].
The mathematical theory of probability is the same regardless of one's interpretation of the concept, although the importance attached to various results can depend very much on the interpretation.
Whether we treat the convolution as continuous or discrete, its interpretation is the same: for each position x in the output function, we shift the filter function g left or right until it is centered at that position, we flip it left-to-right, we multiply every point on f by the corresponding point on our shifted g, and we add (or integrate) these products together.
Nevertheless, the interpretation is the same as before: a negative response of spending to a tax cut is supportive of the starve-the-beast hypothesis; a positive response or no response is not.
This negative effect is also in line with the low-skilled employment rate's findings, and its interpretation is the same as in the case of the employment and unemployment rates.
This leads us to define the symmetric 95% confidence interval: \[ CI_{95} = [ \hat{\theta} - \Delta, \hat{\theta} + \Delta ] \] The interpretation is the same as in the foregoing: with repeated sampling we find the true value within $\Delta$ of the estimate in 95% of all samples.
The interpretation is the same at the individual level as at the departmental level, and comparable to Cronbach's alpha coefficient [ 24, 25]: values range between 0 and 1, with a higher score representing a more reliable scale.
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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