The sleeper effect: Artifact or phenomenon—A brief comment on Bell et al. (2013)

OBJECTIVE: Bell, Marcus, and Goodlad (2013) recently conducted a meta-analysis of randomized controlled additive trials and found that adding an additional component to an existing treatment vis-à-vis the existing treatment produced larger effect sizes on targeted outcomes at 6-months follow-up than at termination, an effect they labeled as a sleeper effect. One of the limitations with Bell et al.'s detection of the sleeper effect was that they did not conduct a statistical test of the size of the effect at follow-up versus termination. METHOD: To statistically test if the differences of effect sizes between the additive conditions and the control conditions at follow-up differed from those at termination, we used a restricted maximum-likelihood random-effect model with known variances to conduct a multilevel longitudinal meta-analysis (k = 30). RESULTS: Although the small effects at termination detected by Bell et al. were replicated (ds = 0.17-0.23), none of the analyses of growth from termination to follow-up produced statistically significant effects (ds < 0.08; p > .20), and when asymmetry was considered using trim-and-fill procedure or the studies after 2000 were analyzed, magnitude of the sleeper effect was negligible (d = 0.00). CONCLUSION: There is no empirical evidence to support the sleeper effect.

LTE uplink scheduling - flow level analysis

Long Term Evolution (LTE) is a cellular technology foreseen to extend the capacity and improve the performance of current 3G cellular networks. A key mechanism in the LTE traffic handling is the packet scheduler, which is in charge of allocating resources to active flows in both the frequency and time dimension. In this paper we present a performance comparison of three distinct scheduling schemes for LTE uplink with main focus on the impact of flow-level dynamics resulting from the random user behaviour. We apply a combined analytical/simulation approach which enables fast evaluation of flow-level performance measures. The results show that by considering flow-level dynamics we are able to observe performance trends that would otherwise stay hidden if only packet-level analysis is performed.

Continuous flow left ventricular assist devices: a valid option for heart failure patients

Recent outstanding clinical advances with new mechanical circulatory systems (MCS) have led to additional strategies in the treatment of end stage heart failure (HF). Heart transplantation (HTx) can be postponed and for certain patients even replaced by smaller implantable left ventricular assist devices (LVAD). Mechanical support of the failing left ventricle enables appropriate hemodynamic stabilisation and recovery of secondary organ failure, often seen in these severely ill patients. These new devices may be of great help to bridge patients until a suitable cardiac allograft is available but are also discussed as definitive treatment for patients who do not qualify for transplantation. Main indications for LVAD implantation are bridge to recovery, bridge to transplantation or destination therapy. LVAD may be an important tool for patients with an expected prolonged period on the waiting list, for instance those with blood group 0 or B, with a body weight over 90 kg and those with potentially reversible secondary organ failure and pulmonary artery hypertension. However, LVAD implantation means an additional heart operation with inherent peri-operative risks and complications during the waiting period. Finally, cardiac transplantation in patients with prior implantation of a LVAD represents a surgical challenge. This review summarises the current knowledge about LVAD and continuous flow devices especially since the latter have been increasingly used worldwide in the most recent years. The review is also based on the institutional experience at Berne University Hospital between 2000 and 2012. Apart from short-term devices (Impella, Cardiac Assist, Deltastream and ECMO) which were used in approximately 150 cases, 85 pulsatile long-term LVAD, RVAD or bi-VAD and 44 non-pulsatile LVAD (mainly HeartMateII and HeartWare) were implanted. After an initial learning curve, one-year mortality dropped to 10.4% in the last 58 patients.