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In many cases, until a baseline data set is well established, there is not a great deal that can be safely concluded without conducting a fairly careful risk assessment.
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In all studies, a uniform baseline data set was obtained.
The results of the baseline data sets are depicted in Figures 6 and 7.
The performances of the different baseline data sets are consistent with the results of Agarwal et al. [23] for a binary labeling of training and test sets.
One hundred unique baseline data sets were generated.
After surgery and a 90 min recovery period baseline data sets were collected.
The QoL post-baseline data set was comprised of 217 patients: 123 controlled and 94 uncontrolled.
Any missing post-baseline QoL data for patients in the QoL post-baseline data set were extrapolated using the last observation carried forward (LOCF) method for analysis purposes.
The reverberant speech of the baseline multi-condition training data set is thus relatively close to that of the SimData test sets.
This data set is characterized by effective baselines ranging from 182 to 75 m with a corresponding height of ambiguity of 38 113 m (details: Supplementary Table 2).
The criteria for selecting the baseline sample from the final data set was that, for each CDT, patients should have utilized a supply of at least 90 days and should have maintained a Medication Possession Ratio (MPR) of at least 0.5.
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