No abstract
Objective: Chronic subdural hematoma (CSDH) is a common form of intracranial hemorrhage with a substantial recurrence rate. Atorvastatin may reduce CSDH via its anti-inflammatory and pro-angiogenesis effects, but its effectiveness for preventing recurrent CSDH has never been explored. We hypothesized that atorvastatin is effective in reducing recurrence of CSDH after surgery and identified determining factors predictive of hematoma recurrence.Methods: A prospective study was conducted in 168 surgical cases of CSDH.All patients were randomly assigned to the group treated with atorvastatin or control group. Clinically relevant data were compared between two groups, and subsequently between the recurrence and non-recurrence patients. Multiple logistic regression analysis of the relationship between atorvastatin treatment and the recurrence using brain atrophy, septated and bilateral hematoma was performed.Results: Atorvastatin group conferred an advantage by significantly decreasing the recurrence rate (P = 0.023), and patients managed with atorvastatin also had a longer time-to-recurrence (P = 0.038). Admission brain atrophy and bilateral hematoma differed significantly between the recurrence and non-recurrence patients (P = 0.047 and P = 0.045). The results of logistic regression analysis showed that atorvastatin significantly reduced the probability of recurrence; severe brain atrophy and bilateral hematoma were independent risk factors for recurrent CSDH.Conclusions: Atorvastatin administration may decrease the risks of recurrence.Patients with severe brain atrophy and bilateral CSDH are prone to the recurrence.
Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e.g., anaphora and ellipsis. Existing maximum likelihood estimation based methods often get trapped in easily learned tokens as all tokens are treated equally during training. In this work, we introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the minimum Levenshtein distance through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. To train RISE, we devise an Iterative Reinforce Training (IRT) algorithm with a Dynamic Programming based Sampling (DPS) process to improve exploration. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods and generalizes well on unseen data.
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