Massive stars at redshifts z 6 are predicted to have played a pivotal role in cosmological reionization as luminous sources of ultraviolet (UV) photons. However, the remnants of these massive stars could be equally important as X-ray-luminous (L X ∼ 10 38 erg s −1 ) high-mass X-ray binaries (HMXBs). Because the absorption cross section of neutral hydrogen decreases sharply with photon energy (σ ∝ E −3 ), X-rays can escape more freely than UV photons from the star-forming regions in which they are produced, allowing HMXBs to make a potentially significant contribution to the ionizing X-ray background during reionization. In this paper, we explore the ionizing power of HMXBs at redshifts z 6 using a Monte Carlo model for a coeval stellar population of main-sequence stars and HMXBs. Using the archetypal Galactic HMXB Cygnus X-1 as our template, we propose a composite HMXB spectral energy distribution consisting of blackbody and power-law components, whose contributions depend on the accretion state of the system. We determine the time-dependent ionizing power of a combined population of UV-luminous stars and X-ray-luminous HMXBs and deduce fitting formulae for the boost in the population's ionizing power arising from HMXBs; these fits allow for simple implementation of HMXB feedback in numerical simulations. Based on this analysis, we estimate the contribution of high-redshift HMXBs to the present-day soft X-ray background, and we show that it is a factor of ∼100-1000 smaller than the observed limit. Finally, we discuss the implications of our results for the role of HMXBs in reionization and in high-redshift galaxy formation.
Background: Updated, robust estimates of the incidence and prevalence of rare long-term neurological conditions in the UK are not available. Global estimates may be misrepresentative as disease aetiology may vary by location. Objectives: To systematically review the incidence and prevalence of long-term neurological conditions in the UK since 1988. Search Strategy: Medline (January 1988 to January 2009), Embase (January 1988 to January 2009), CINAHL (January 1988 to January 2009) and Cochrane CENTRAL databases. Selection Criteria: UK population-based incidence/prevalence studies of long-term neurological conditions since 1988. Exclusion criteria included inappropriate diagnoses and incomprehensive case ascertainment. Data Collection and Analysis: Articles were included based on the selection criteria. Data were extracted from articles with ranges of incidence and prevalence reported. Main Results: Eight studies met the criteria (3 on motor neurone disease; 4 on Huntington’s disease; 1 on progressive supranuclear palsy). The incidence of motor neurone disease ranged from 1.06 to 2.4/100,000 person-years. The prevalence ranged from 4.02 to 4.91/100,000. The prevalence of Huntington’s disease ranged from 4.0 to 9.94/100,000. The prevalence of progressive supranuclear palsy ranged from 3.1 to 6.5/100,000. Conclusions: The review updates the incidence/prevalence of long-term neurological conditions. Future epidemiological studies must incorporate comprehensive case ascertainment methods and strict diagnostic criteria.
ObjectiveTo evaluate the effectiveness of physiotherapy intervention following lumbar spinal fusion.DesignSystematic review and meta-analysis. 2 independent reviewers searched information sources, assessed studies for inclusion and evaluated risk of bias. Quantitative synthesis using standardised mean differences was conducted on comparable outcomes across trials with similar interventions.Information sourcesPredefined terms were employed to search electronic databases. Additional studies were identified from key journals, reference lists, authors and experts.Eligibility criteria for included studiesRandomised control trials published in English prior to 30 September 2011 investigating physiotherapy outpatient management of patients (>16 years), following lumbar spinal fusion, with measurements reported on one or more outcome of disability, function and health were included.Results2 Randomised control trials (188 participants) from two countries were included. Both trials included a behavioural and an exercise intervention. 1 trial was evaluated as high risk of bias and one as unclear. 159 participants were incorporated in the meta-analysis. Although evidence from both trials suggested that intervention might reduce back pain short term (6 months) and long term (12 months and 2 years), and a behavioural intervention might be more beneficial than an exercise intervention, the pooled effects (0.72, 95% CI −0.25 to 1.69 at 6 months; 0.52, 95% CI −0.45 to 1.49 at 12 months and 0.75, 95% CI −0.46 to 1.96 at 2 years) did not demonstrate statistically significant effects. There was no evidence that intervention changes pain in the short (6 months) or long term (12 months and 2 years). The wide CI for pooled effects indicated that intervention could be potentially beneficial or harmful. Considerable heterogeneity was evident.ConclusionsInconclusive, very low-quality evidence exists for the effectiveness of physiotherapy management following lumbar spinal fusion. Best practice remains unclear. Limited comparability of outcomes and retrieval of only two trials reflect a lack of research in this area that requires urgent consideration.
The primary objective of this study was to explore clinical reasoning in physical therapy and to highlight the similarities and differences by modelling the diagnostic phase of clinical reasoning. An experimental design comparing expert and novice physical therapists was utilized. Concurrent verbal protocols detailing the clinical reasoning about standardized case material were elicited. A framework for modelling diagnosis was specified and provided the parameters for analysis. The diagnostic utterances were classified as cues or hypotheses and the knowledge utilized was identified. The experts recruited significantly more knowledge than the novices (p = 0.01) and used more cues (p < 0.01). Their diagnoses were more accurate when compared to the original diagnosis. This difference between the experts and novices was reflected in the differences shown in the models (p < 0.01). The differences between these subjects focused upon the knowledge recruitment, which impacted on the accuracy of the diagnosis. The novices' inaccurate or non-existent diagnoses led to poor quality of treatment prescription. Modelling proved to be a useful way of representing these differences.
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