The efficacy of indwelling pleural catheter placement versus placement plus talc sclerosant in patients with malignant pleural effusions managed exclusively as outpatients (IPC-PLUS): study protocol for a randomised controlled trial

BACKGROUND: Malignant pleural effusions (MPEs) remain a common problem, with 40,000 new cases in the United Kingdom each year and up to 250,000 in the United States. Traditional management of MPE usually involves an inpatient stay with placement of a chest drain, followed by the instillation of a pleural sclerosing agent such as talc, which aims to minimise further fluid build-up. Despite a good success rate in studies, this approach can be expensive, time-consuming and inconvenient for patients. More recently, an alternative method has become available in the form of indwelling pleural catheters (IPCs), which can be inserted and managed in an outpatient setting. It is currently unknown whether combining talc pleurodesis with IPCs will provide improved pleural symphysis rates over those of IPCs alone. METHODS/DESIGN: IPC-PLUS is a patient-blind, multicentre randomised controlled trial (RCT) comparing the combination of talc with an IPC to the use of an IPC alone for inducing pleurodesis in MPEs. The primary outcome is successful pleurodesis at five weeks post-randomisation. This study will recruit 154 patients, with an interim analysis for efficacy after 100 patients, and aims to help to define the future gold standard for outpatient management of patients with symptomatic MPEs. DISCUSSION: IPC-PLUS is the first RCT to examine the practicality and utility of talc administered via an IPC. The study remains in active recruitment and has the potential to significantly alter how patients requiring pleurodesis for MPE are approached in the future. TRIAL REGISTRATION: This trial was registered with Current Controlled Trials (identifier: ISRCTN73255764) on 23 August 2012

Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest

Eddy covariance (EC) datasets have provided insight into climate determinants of net ecosystem productivity (NEP) and evapotranspiration (ET) in natural ecosystems for decades, but most EC studies were published in serial fashion such that one study's result became the following study's hypothesis. This approach reflects the hypothetico-deductive process by focusing on previously derived hypotheses. A synthesis of this type of sequential inference reiterates subjective biases and may amplify past assumptions about the role, and relative importance, of controls over ecosystem metabolism. Long-term EC datasets facilitate an alternative approach to synthesis: the use of inductive data-based analyses to re-examine past deductive studies of the same ecosystem. Here we examined the seasonal climate determinants of NEP and ET by analyzing a 15-year EC time-series from a subalpine forest using an ensemble of Artificial Neural Networks (ANNs) at the half-day (daytime/nighttime) time-step. We extracted relative rankings of climate drivers and driver-response relationships directly from the dataset with minimal a priori assumptions. The ANN analysis revealed temperature variables as primary climate drivers of NEP and daytime ET, when all seasons are considered, consistent with the assembly of past studies. New relations uncovered by the ANN approach include the role of soil moisture in driving daytime NEP during the snowmelt period, the nonlinear response of NEP to temperature across seasons, and the low relevance of summer rainfall for NEP or ET at the same daytime/nighttime time step. These new results offer a more complete perspective of climate-ecosystem interactions at this site than traditional deductive analyses alone

Application of ANN and ANFIS for Predicting the Ultimate Bearing Capacity of Eccentrically Loaded Rectangular Foundations

Extensive laboratory model tests were conducted on a rectangular embedded foundation resting over homogeneous sand bed and subjected to an eccentric load to determine the ultimate bearing capacity. Tests were conducted for foundations with width-to-length ratios (B/L) of zero (strip case), 0.333, 0.5, and 1. The depth of embedment varies from 0 to 1 B with an increment of 0.5 B; where B is the width of foundation and the eccentricity ratio (e/B) varies from 0 to 0.15 with an increment of 0.05. Based on the laboratory model test results, two different approaches are proposed to determine the ultimate bearing capacity. Firstly, a neural network model is developed to estimate the reduction factor. The reduction factor can be used to estimate the ultimate bearing capacity of an eccentrically loaded foundation from the ultimate bearing capacity of a centrally loaded foundation. A thorough sensitivity analysis was carried out to determine the important parameters affecting the reduction factor. Importance was given to the construction of neural interpretation diagram. Based on this diagram, whether direct or inverse relationships exist between the input and output parameters were determined. Secondly, an adaptive neuro-fuzzy interface system (ANFIS) is used to predict the ultimate bearing capacity. The neuro-fuzzy models combine the transparent, linguistic representation of a fuzzy system with learning ability of artificial neural networks (ANNs). The results from the ANN and ANFIS were compared with the laboratory model test results. It is clearly seen that the performance of the ANFIS model in our study is better than that of the ANN model

Spirituality, leadership and values in the NHS

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