Bmc Med
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Surgery is a common treatment strategy for patients with neurofibromatosis type 1 (NF1)-related plexiform neurofibroma (PN) and has limited efficacy. FCN-159 is a novel anti-tumorigenic drug via selective inhibition of MEK1/2. This study assesses the safety and efficacy of FCN-159 in patients with NF1-related PN. ⋯ FCN-159 was well tolerated up to 8 mg daily with manageable adverse events and showed promising anti-tumorigenic activity in patients with NF1-related PN, warranting further investigation in this indication.
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In addition to improving survival outcomes, new oncology treatments should lead to amelioration of patients' quality of life (QoL). Herein, we examined whether QoL results correlated with PFS and OS outcomes in phase III randomized controlled trials (RCTs) investigating new systemic treatments in metastatic non-small cell lung cancer (NSCLC). ⋯ Our study reveals a positive association of QoL results with PFS outcomes in RCTs testing novel treatments in metastatic NSCLC. This association is particularly evident for target therapies. These findings further emphasize the relevance of an accurate assessment of QoL in RCTs in NSCLC.
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Post-acute care (PAC) services after hospitalization for hip fracture are typically provided in skilled nursing facilities (SNFs), inpatient rehabilitation facilities (IRFs), or at home via home health care (HHC). Little is known about the clinical course following PAC for hip fracture. We examined the nationwide burden of adverse outcomes by PAC setting in the year following discharge from PAC for hip fracture. ⋯ In this retrospective cohort study of individuals hospitalized for hip fracture, rates of adverse outcomes in the year following PAC were common, especially among SNF care recipients. Understanding risks and rates of adverse events can inform future efforts to improve outcomes for older adults receiving PAC for hip fracture. Future work should consider calculating risk and rate measures to assess the influence of differential time under observation across PAC groups.
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The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. ⋯ Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.