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Both of these methods had high success rates overall (77% and 86%), but attempts under direct visualization had lower success rates with removing spherical objects, objects touching the tympanic membrane, and objects in the canal for more than 24 hours.
In Emerald, the LGA with the smallest population and number of notifications, all of the methods had high FN rates ranging from 45% to 100% (Table 4).
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All the five machine learning methods had higher sensitivities but lower specificities than human doctors.
As expected, laboratories using primarily rapid antigen tests had lower estimated sensitivities, and laboratories that used PCR methods had higher sensitivity estimates.
The combination of methods had higher sensitivity (88.0% vs 67.4%) but reduced specificity (88.0% vs 69.5%).
With the equicorrelated variance structure, EE(Wald), SL and ML methods had higher power than the other four methods.
Spectral methods have high accuracy[4, 5, 6, 7, 8].
However, these methods have high operating cost [2] or are inefficient due to complex aromatic structure.
Iterative methods have high computational cost, since most of them use filter methods in iterative ways or in combination with machine learning techniques.
As mentioned previously, in biology it is desired that methods have high precision even in the expense of recall (completeness).
These methods have high computational complexity and yield approximate solutions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com