Background In advanced cancer, patients want to know how their care options may affect survival and quality of life, but the impact of outpatient specialty palliative care on these outcomes in cancer is uncertain. Purpose To estimate the impact of outpatient specialty palliative care programs on survival and quality of life in adults with advanced cancer. Methods Following PRISMA guidelines, we conducted a systematic review and meta-analysis of randomized controlled trials comparing outpatient specialty palliative care with usual care in adults with advanced cancer. Primary outcomes were 1 year survival and quality of life. Analyses were stratified to compare preliminary studies against higher-quality studies. Secondary outcomes were survival at other endpoints and physical and psychological quality-of-life measures. Results From 2,307 records, we identified nine studies for review, including five high-quality studies. In the three high-quality studies with long-term survival data (n = 646), patients randomized to outpatient specialty palliative care had a 14% absolute increase in 1 year survival relative to controls (56% vs. 42%, p < .001). The survival advantage was also observed at 6, 9, 15, and 18 months, and median survival was 4.56 months longer (14.55 vs. 9.99 months). In the five high-quality studies with quality-of-life data (n = 1,398), outpatient specialty palliative care improved quality-of-life relative to controls (g = .18, p < .001), including for physical and psychological measures. Conclusions Patients with advanced cancer randomized to receive outpatient specialty palliative care lived longer and had better quality of life. Findings have implications for improving care in advanced cancer.

An implementation of the replica exchange with dynamical scaling (REDS) method in the commonly used molecular dynamics program GROMACS is presented. REDS is a replica exchange method that requires fewer replicas than conventional replica exchange while still providing data over a range of temperatures and can be used in either constant volume or constant pressure ensembles. Details for running REDS simulations are given, and an application to the human islet amyloid polypeptide (hIAPP) 11-25 fragment shows that the model efficiently samples conformational space.

py-MCMD, an open-source Python software, provides a robust workflow layer that manages communication of relevant system information between the simulation engines NAMD and GOMC and generates coherent thermodynamic properties and trajectories for analysis. To validate the workflow and highlight its capabilities, hybrid Monte Carlo/molecular dynamics (MC/MD) simulations are performed for SPC/E water in the isobaric–isothermal (NPT) and grand canonical (GC) ensembles as well as with Gibbs ensemble Monte Carlo (GEMC). The hybrid MC/MD approach shows close agreement with reference MC simulations and has a computational efficiency that is 2 to 136 times greater than traditional Monte Carlo simulations. MC/MD simulations performed for water in a graphene slit pore illustrate significant gains in sampling efficiency when the coupled–decoupled configurational-bias MC (CD–CBMC) algorithm is used compared with simulations using a single unbiased random trial position. Simulations using CD–CBMC reach equilibrium with 25 times fewer cycles than simulations using a single unbiased random trial position, with a small increase in computational cost. In a more challenging application, hybrid grand canonical Monte Carlo/molecular dynamics (GCMC/MD) simulations are used to hydrate a buried binding pocket in bovine pancreatic trypsin inhibitor. Water occupancies produced by GCMC/MD simulations are in close agreement with crystallographically identified positions, and GCMC/MD simulations have a computational efficiency that is 5 times better than MD simulations. py-MCMD is available on GitHub at .

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