Do the UK government's new Quality and Outcomes Framework (QOF) scores adequately measure primary care performance? A cross-sectional survey of routine healthcare data

BACKGROUND General practitioners' remuneration is now linked directly to the scores attained in the Quality and Outcomes Framework (QOF). The success of this approach depends in part on designing a robust and clinically meaningful set of indicators. The aim of this study was to assess the extent to which measures of health observed in practice populations are correlated with their QOF scores, after accounting for the established associations between health outcomes and socio-demographics. METHODS QOF data for the period April 2004 to March 2005 were obtained for all general practices in two English Primary Care Trusts. These data were linked to data for emergency hospital admissions (for asthma, cancer, chronic obstructive pulmonary disease, coronary hear disease, diabetes, stroke and all other conditions) and all cause mortality for the period September 2004 to August 2005. Multilevel logistic regression models explored the association between health outcomes (hospital admission and death) and practice QOF scores (clinical, additional services and organisational domains), age, sex and socio-economic deprivation. RESULTS Higher clinical domain scores were generally associated with lower admission rates and this was significant for cancer and other conditions in PCT 2. Higher scores in the additional services domain were associated with higher admission rates, significantly so for asthma, CHD, stroke and other conditions in PCT 1 and cancer in PCT 2. Little association was observed between the organisational domain scores and admissions. The relationship between the QOF variables and mortality was less clear. Being female was associated with fewer admissions for cancer and CHD and lower mortality rates. Increasing age was mainly associated with an increased number of events. Increasing deprivation was associated with higher admission rates for all conditions and with higher mortality rates. CONCLUSION The associations between QOF scores and emergency admissions and mortality were small and inconsistent, whilst the impact of socio-economic deprivation on the outcomes was much stronger. These results have implications for the use of target-based remuneration of general practitioners and emphasise the need to tackle inequalities and improve the health of disadvantaged groups and the population as a whole

Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities. In this paper, we present an iterative multi-class learning method to segment the femoral, tibial and patellar cartilage simultaneously, which effectively exploits the spatial contextual constraints between bone and cartilage, and also between different cartilages. First, based on the fact that the cartilage grows in only certain area of the corresponding bone surface, we extract the distance features of not only to the surface of the bone, but more informatively, to the densely registered anatomical landmarks on the bone surface. Second, we introduce a set of iterative discriminative classifiers that at each iteration, probability comparison features are constructed from the class confidence maps derived by previously learned classifiers. These features automatically embed the semantic context information between different cartilages of interest. Validated on a total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the proposed approach demonstrates high robustness and accuracy of segmentation in comparison with existing state-of-the-art MR cartilage segmentation methods.Comment: MICCAI 2013: Workshop on Medical Computer Visio

Decoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities

Pain is a highly subjective experience. Self-report is the gold standard for pain assessment in clinical practice, but it may not be available or reliable in some populations. Neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have the potential to be used to provide physiology-based and quantitative nociceptive pain assessment tools that complements self-report. However, existing neuroimaging-based nociceptive pain assessments only rely on the information in pain-evoked brain activities, but neglect the fact that the perceived intensity of pain is also encoded by ongoing brain activities prior to painful stimulation. Here, we proposed to use machine learning algorithms to decode pain intensity from both pre-stimulus ongoing and post-stimulus evoked brain activities. Neural features that were correlated with intensity of laser-evoked nociceptive pain were extracted from high-dimensional pre- and post-stimulus EEG and fMRI activities using partial least-squares regression (PLSR). Further, we used support vector machine (SVM) to predict the intensity of pain from pain-related time-frequency EEG patterns and BOLD-fMRI patterns. Results showed that combining predictive information in pre- and post-stimulus brain activities can achieve significantly better performance in classifying high-pain and low-pain and in predicting the rating of perceived pain than only using post-stimulus brain activities. Therefore, the proposed pain prediction method holds great potential in basic research and clinical applications.published_or_final_versio

Front Stability in Mean Field Models of Diffusion Limited Growth

We present calculations of the stability of planar fronts in two mean field models of diffusion limited growth. The steady state solution for the front can exist for a continuous family of velocities, we show that the selected velocity is given by marginal stability theory. We find that naive mean field theory has no instability to transverse perturbations, while a threshold mean field theory has such a Mullins-Sekerka instability. These results place on firm theoretical ground the observed lack of the dendritic morphology in naive mean field theory and its presence in threshold models. The existence of a Mullins-Sekerka instability is related to the behavior of the mean field theories in the zero-undercooling limit.Comment: 26 pp. revtex, 7 uuencoded ps figures. submitted to PR

Transient quantum transport in double-dot Aharonov-Bohm interferometers

Real-time nonequilibrium quantum dynamics of electrons in double-dot Aharonov-Bohm (AB) interferometers is studied using an exact solution of the master equation. The building of the coherence between the two electronic paths shows up via the time-dependent amplitude of the AB oscillations in the transient transport current, and can be enhanced by varying the applied bias on the leads, the on-site energy difference between the dots and the asymmetry of the coupling of the dots to the leads. The transient oscillations of the transport current do not obey phase rigidity. The circulating current has an anti-symmetric AB oscillation in the flux. The non-degeneracy of the on-site energies and the finite bias cause the occupation in each dot to have an arbitrary flux dependence as the coupling asymmetry is varied.Comment: 11 pages, 5 figure

Entangling two superconducting LC coherent modes via a superconducting flux qubit

Based on a pure solid-state device consisting of two superconducting LC circuits coupled to a superconducting flux qubit, we propose in this paper that the maximally entangled coherent states of the two LC modes can be generated for arbitrary coherent states through flux qubit controls.Comment: 5 pages, 2 figure

Activatable T1 and T2 Magnetic Resonance Imaging Contrast Agents

Magnetic resonance imaging (MRI) has become one of the most important diagnosis tools available in medicine. Typically MRI is not capable of sensing biochemical activities. However, recently emerged activatable MRI contrast agents (CAs), whose relaxivity is variable in response to a specific parameter change in the surrounding physiological microenvironment, potentially allow for MRI to indicate biological processes. Among the various factors influencing the relaxivity of a CA, the number of inner-sphere water molecules (q) directly coordinated to the metal center, the residence time of the coordinated water molecule (τm), and the rotational correlation time representing the molecular tumbling time of a complex (τR) contribute strongly to the relaxivity of an activatable CA. Tuning the ligand structure and properties has been the subject of intensive research for activatable MR CA designs. This review summarizes a variety of activatable MRI CAs sensitive to common variables in microenvironment in vivo, i.e., pH, luminescence, metal ions, redox, and enzymes, etc., with emphasis on the influence of ligand design on parameters q, τm, and τR

Pulsed THz radiation due to phonon-polariton effect in [110] ZnTe crystal

Pulsed terahertz (THz) radiation, generated through optical rectification (OR) by exciting [110] ZnTe crystal with ultrafast optical pulses, typically consists of only a few cycles of electromagnetic field oscillations with a duration about a couple of picoseconds. However, it is possible, under appropriate conditions, to generate a long damped oscillation tail (LDOT) following the main cycles. The LDOT can last tens of picoseconds and its Fourier transform shows a higher and narrower frequency peak than that of the main pulse. We have demonstrated that the generation of the LDOT depends on both the duration of the optical pulse and its central wavelength. Furthermore, we have also performed theoretical calculations based upon the OR effect coupled with the phonon-polariton mode of ZnTe and obtained theoretical THz waveforms in good agreement with our experimental observation.Comment: 9 pages, 5 figure