In univariate analysis, the age at recurrence, the size of the largest recurrent tumors, the site of recurrence (retroperitoneal or not), and multiplicity of recurrence were associated with optimal cytoreduction (R0 and R1) (P = 0.013, 0.022, 0.086, and 0.03, respectively).
Multivariate analysis showed that the age at recurrence, the size of the largest recurrent tumors, and multiplicity of recurrence were associated with the optimal cytoreduction (P = 0.046, 0.028, and 0.044, respectively) (Table 3).
In univariate analysis, the age, PFI, the size of the largest recurrent tumors, the site of recurrence, and multiplicity of recurrence were associated with optimal cytoreduction, which indicates that patients with younger age, longer PFI, smaller tumor size, and recurrent site not in retroperitoneal, and single recurrence are more likely to achieve optimal cytoreduction.
However, in multivariate analysis, only the age, the size of the largest recurrent tumors, and multiplicity of recurrence were associated with the optimal cytoreduction.
The size of the largest recurrent tumors (≤6 cm), solitary recurrent tumor, and age at recurrence (≤56 years old) were associated with optimal SCR.
The largest recurrent changes were gain of 16p and 20q but most changes involved much smaller genomic regions scattered throughout the genomes.
The court explained that "[r]ed flags may exist if the complaint alleges the existence of transactions that were large, recurrent, timed near the end of a reporting period, and obviously lacking in business purpose.
We present two patients with large, recurrent supraclinoid internal carotid artery (ICA) aneurysms which were previously clipped 17 and 23 years ago at outside institutions.
In this paper, we describe a simulation approach (SIERRA) focussed on the particular case of Mediterranean-type communities subjected to large recurrent fires.
More specifically, this technique is used to build a large recurrent neural network that allows one to create and evaluate nonlinear relationships between X c (t i ) and N c (t r ) [77].
In their newly published work, they looked at using outputs from a large recurrent neural network as labels to train a small DNN and achieved a large reduction in WER compared to training the small DNN with hard targets from a forced alignment on the 81 hour Wall Street Journal corpus of read speech.
