Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach

Prognostic models play an important role in the clinical management of cervical radiculopathy (CR). No study has compared the performance of modern machine learning techniques, against more traditional stepwise regression techniques, when developing prognostic models in individuals with CR. We analysed a prospective cohort dataset of 201 individuals with CR. Four modelling techniques (stepwise regression, least absolute shrinkage and selection operator [LASSO], boosting, and multivariate adaptive regression splines [MuARS]) were each used to form a prognostic model for each of four outcomes obtained at a 12 month follow-up (disability—neck disability index [NDI]), quality of life (EQ5D), present neck pain intensity, and present arm pain intensity). For all four outcomes, the differences in mean performance between all four models were small (difference of NDI < 1 point; EQ5D < 0.1 point; neck and arm pain < 2 points). Given that the predictive accuracy of all four modelling methods were clinically similar, the optimal modelling method may be selected based on the parsimony of predictors. Some of the most parsimonious models were achieved using MuARS, a non-linear technique. Modern machine learning methods may be used to probe relationships along different regions of the predictor space

Optimal Cyanobacterial Pigment Retrieval from Ocean Colour Sensors in a Highly Turbid, Optically Complex Lake

To date, several algorithms for the retrieval of cyanobacterial phycocyanin (PC) from ocean colour sensors have been presented for inland waters, all of which claim to be robust models. To address this, we conducted a comprehensive comparison to identify the optimal algorithm for retrieval of PC concentrations in the highly optically complex waters of Lake Balaton (Hungary). MEdium Resolution Imaging Spectrometer (MERIS) top-of-atmosphere radiances were first atmospherically corrected using the Self-Contained Atmospheric Parameters Estimation for MERIS data v.B2 (SCAPE-M_B2). Overall, the Simis05 semi-analytical algorithm outperformed more complex inversion algorithms, providing accurate estimates of PC up to ±7 days from the time of satellite overpass during summer cyanobacteria blooms (RMSElog 0.66, p < 0.001). In-depth analysis of the Simis05 algorithm using in situ measurements of inherent optical properties (IOPs) revealed that the Simis05 model overestimated the phytoplankton absorption coefficient [aph(λ)] by a factor of ~2. However, these errors were compensated for by underestimation of the mass-specific chlorophyll absorption coefficient [a*chla(λ)]. This study reinforces the need for further validation of algorithms over a range of optical water types in the context of the recently launched Ocean Land Colour Instrument (OLCI) onboard Sentinel-3

Probing the mechanisms underpinning recovery in post‐surgical patients with cervical radiculopathy using Bayesian networks

Background Rehabilitation approaches should be based on an understanding of the mechanisms underpinning functional recovery. Yet, the mediators that drive an improvement in post‐surgical pain‐related disability in individuals with cervical radiculopathy (CR) are unknown. The aim of the present study is to use Bayesian networks (BN) to learn the probabilistic relationships between physical and psychological factors, and pain-related disability in CR. Methods We analysed a prospective cohort dataset of 201 post‐surgical individuals with CR. In all, 15 variables were used to build a BN model: age, sex, neck muscle endurance, neck range of motion, neck proprioception, hand grip strength, self-efficacy, catastrophizing, depression, somatic perception, arm pain intensity, neck pain intensity and disability. Results A one point increase in a change of self‐efficacy at 6 months was associated with a 0.09 point decrease in a change in disability at 12 months (t = −64.09, p < .001). Two pathways led to a change in disability: a direct path leading from a change in self-efficacy at 6 months to disability, and an indirect path which was mediated by neck and arm pain intensity changes at 6 and 12 months. Conclusions This is the first study to apply BN modelling to understand the mechanisms of recovery in post‐surgical individuals with CR. Improvements in pain‐related disability was directly and indirectly driven by changes in self‐efficacy levels. The present study provides potentially modifiable mediators that could be the target of future intervention trials. BN models could increase the precision of treatment and outcome assessment of individuals with CR. Significance Using Bayesian Network modelling, we found that changes in self-efficacy levels at 6-month post-surgery directly and indirectly influenced the change in disability in individuals with CR. A mechanistic understanding of recovery provides potentially modifiable mediators that could be the target of future intervention trials

Pilot analysis of the usefulness of mortality risk score systems at resuscitated patients

Introduction: Sudden cardiac death is one of the most significant cardiovascular causes of death worldwide. Although there have been immense methodological and technical advances in the field of cardiopulmonary resuscitation and following intensive care in the last decade, currently there are only a few validated risk-stratification scoring systems for the quick and reliable estimation of the mortality risk of these patients at the time of admission to the intensive care unit. Objective: Our aim was to correlate the mortality prediction risk points calculated by CardShock Risk Score (CSRS) and modified (m) CSRS based on the admission data of the post-cardiac arrest syndrome (PCAS) patients. Methods: The medical records of 172 out-of-hospital resuscitated cardiac arrest patients, who were admitted at the Heart and Vascular Centre of Semmelweis University, were screened retrospectively. Out of the 172 selected patients, 123 were eligible for inclusion to calculate CSRS and mCSRS. Based on CSRS score, we generated three different groups of patients, with scores 1 to 3, 4 to 6, and 7+, respectively. Mortality data of the groups were compared by log-rank test. Results: Mean age of the patients was 63.6 years (69% male), the cause of sudden cardiac death was acut coronary syndrome in 80% of the cases. The early and late mortality was predicted by neurological status, serum lactate level, renal function, initial rhythm, and the need of catecholamines. Using mCSRS, a significant survival difference was proven in between the groups "1-3" vs "4-6" (p Conclusion: Compared to the CSRS, the mCSRS expanded with the 2 additional weighting points differentiates more specifically the low-moderate and high survival groups in the PCAS patient population treated in our institute.Peer reviewe

Modeling of GERDA Phase II data

The GERmanium Detector Array (GERDA) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double-beta ($0\nu\beta\beta$) decay of $^{76}$Ge. The technological challenge of GERDA is to operate in a "background-free" regime in the region of interest (ROI) after analysis cuts for the full 100$\,$kg$\cdot$yr target exposure of the experiment. A careful modeling and decomposition of the full-range energy spectrum is essential to predict the shape and composition of events in the ROI around $Q_{\beta\beta}$ for the $0\nu\beta\beta$ search, to extract a precise measurement of the half-life of the double-beta decay mode with neutrinos ($2\nu\beta\beta$) and in order to identify the location of residual impurities. The latter will permit future experiments to build strategies in order to further lower the background and achieve even better sensitivities. In this article the background decomposition prior to analysis cuts is presented for GERDA Phase II. The background model fit yields a flat spectrum in the ROI with a background index (BI) of $16.04^{+0.78}_{-0.85} \cdot 10^{-3}\,$cts/(kg$\cdot$keV$\cdot$yr) for the enriched BEGe data set and $14.68^{+0.47}_{-0.52} \cdot 10^{-3}\,$cts/(kg$\cdot$keV$\cdot$yr) for the enriched coaxial data set. These values are similar to the one of Gerda Phase I despite a much larger number of detectors and hence radioactive hardware components

Modeling of GERDA Phase II data

The GERmanium Detector Array (Gerda) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double-beta (0νββ) decay of 76Ge. The technological challenge of Gerda is to operate in a “background-free” regime in the region of interest (ROI) after analysis cuts for the full 100 kg·yr target exposure of the experiment. A careful modeling and decomposition of the full-range energy spectrum is essential to predict the shape and composition of events in the ROI around Qββ for the 0νββ search, to extract a precise measurement of the half-life of the double-beta decay mode with neutrinos (2νββ) and in order to identify the location of residual impurities. The latter will permit future experiments to build strategies in order to further lower the background and achieve even better sensitivities. In this article the background decomposition prior to analysis cuts is presented for Gerda Phase II. The background model fit yields a flat spectrum in the ROI with a background index (BI) of 16.04+0.78−0.85⋅10−3 cts/(keV·kg·yr) for the enriched BEGe data set and 14.68+0.47−0.52⋅10−3 cts/(keV·kg·yr) for the enriched coaxial data set. These values are similar to the one of Phase I despite a much larger number of detectors and hence radioactive hardware components

Penilaian Kinerja Keuangan Koperasi di Kabupaten Pelalawan

This paper describe development and financial performance of cooperative in District Pelalawan among 2007 - 2008. Studies on primary and secondary cooperative in 12 sub-districts. Method in this stady use performance measuring of productivity, efficiency, growth, liquidity, and solvability of cooperative. Productivity of cooperative in Pelalawan was highly but efficiency still low. Profit and income were highly, even liquidity of cooperative very high, and solvability was good

Juxtaposing BTE and ATE – on the role of the European insurance industry in funding civil litigation

One of the ways in which legal services are financed, and indeed shaped, is through private insurance arrangement. Two contrasting types of legal expenses insurance contracts (LEI) seem to dominate in Europe: before the event (BTE) and after the event (ATE) legal expenses insurance. Notwithstanding institutional differences between different legal systems, BTE and ATE insurance arrangements may be instrumental if government policy is geared towards strengthening a market-oriented system of financing access to justice for individuals and business. At the same time, emphasizing the role of a private industry as a keeper of the gates to justice raises issues of accountability and transparency, not readily reconcilable with demands of competition. Moreover, multiple actors (clients, lawyers, courts, insurers) are involved, causing behavioural dynamics which are not easily predicted or influenced. Against this background, this paper looks into BTE and ATE arrangements by analysing the particularities of BTE and ATE arrangements currently available in some European jurisdictions and by painting a picture of their respective markets and legal contexts. This allows for some reflection on the performance of BTE and ATE providers as both financiers and keepers. Two issues emerge from the analysis that are worthy of some further reflection. Firstly, there is the problematic long-term sustainability of some ATE products. Secondly, the challenges faced by policymakers that would like to nudge consumers into voluntarily taking out BTE LEI