Treatment compliance and effectiveness of a cognitive behavioural intervention for low back pain : a complier average causal effect approach to the BeST data set

Background: Group cognitive behavioural intervention (CBI) is effective in reducing low-back pain and disability in comparison to advice in primary care. The aim of this analysis was to investigate the impact of compliance on estimates of treatment effect and to identify factors associated with compliance. Methods: In this multicentre trial, 701 adults with troublesome sub-acute or chronic low-back pain were recruited from 56 general practices. Participants were randomised to advice (control n = 233) or advice plus CBI (n = 468). Compliance was specified a priori as attending a minimum of three group sessions and the individual assessment. We estimated the complier average causal effect (CACE) of treatment. Results: Comparison of the CACE estimate of the mean treatment difference to the intention-to-treat (ITT) estimate at 12 months showed a greater benefit of CBI amongst participants compliant with treatment on the Roland Morris Questionnaire (CACE: 1.6 points, 95% CI 0.51 to 2.74; ITT: 1.3 points, 95% CI 0.55 to 2.07), the Modified Von Korff disability score (CACE: 12.1 points, 95% CI 6.07 to 18.17; ITT: 8.6 points, 95% CI 4.58 to 12.64) and the Modified von Korff pain score (CACE: 10.4 points, 95% CI 4.64 to 16.10; ITT: 7.0 points, 95% CI 3.26 to 10.74). People who were non-compliant were younger and had higher pain scores at randomisation. Conclusions: Treatment compliance is important in the effectiveness of group CBI. Younger people and those with more pain are at greater risk of non-compliance

The Universe is worth 64364^3 pixels: Convolution Neural Network and Vision Transformers for Cosmology

We present a novel approach for estimating cosmological parameters, $\Omega_m$, $\sigma_8$, $w_0$, and one derived parameter, $S_8$, from 3D lightcone data of dark matter halos in redshift space covering a sky area of $40^\circ \times 40^\circ$ and redshift range of $0.3 < z < 0.8$, binned to $64^3$ voxels. Using two deep learning algorithms, Convolutional Neural Network (CNN) and Vision Transformer (ViT), we compare their performance with the standard two-point correlation (2pcf) function. Our results indicate that CNN yields the best performance, while ViT also demonstrates significant potential in predicting cosmological parameters. By combining the outcomes of Vision Transformer, Convolution Neural Network, and 2pcf, we achieved a substantial reduction in error compared to the 2pcf alone. To better understand the inner workings of the machine learning algorithms, we employed the Grad-CAM method to investigate the sources of essential information in activation maps of the CNN and ViT. Our findings suggest that the algorithms focus on different parts of the density field and redshift depending on which parameter they are predicting. This proof-of-concept work paves the way for incorporating deep learning methods to estimate cosmological parameters from large-scale structures, potentially leading to tighter constraints and improved understanding of the Universe.Comment: 23 pages, 10 figure

Fast Ensemble Smoothing

Smoothing is essential to many oceanographic, meteorological and hydrological applications. The interval smoothing problem updates all desired states within a time interval using all available observations. The fixed-lag smoothing problem updates only a fixed number of states prior to the observation at current time. The fixed-lag smoothing problem is, in general, thought to be computationally faster than a fixed-interval smoother, and can be an appropriate approximation for long interval-smoothing problems. In this paper, we use an ensemble-based approach to fixed-interval and fixed-lag smoothing, and synthesize two algorithms. The first algorithm produces a linear time solution to the interval smoothing problem with a fixed factor, and the second one produces a fixed-lag solution that is independent of the lag length. Identical-twin experiments conducted with the Lorenz-95 model show that for lag lengths approximately equal to the error doubling time, or for long intervals the proposed methods can provide significant computational savings. These results suggest that ensemble methods yield both fixed-interval and fixed-lag smoothing solutions that cost little additional effort over filtering and model propagation, in the sense that in practical ensemble application the additional increment is a small fraction of either filtering or model propagation costs. We also show that fixed-interval smoothing can perform as fast as fixed-lag smoothing and may be advantageous when memory is not an issue

Defining language impairments in a subgroup of children with autism spectrum disorder

Autism spectrum disorder (ASD) is diagnosed on the basis of core impairments in pragmatic language skills, which are found across all ages and subtypes. In contrast, there is significant heterogeneity in language phenotypes, ranging from nonverbal to superior linguistic abilities, as defined on standardized tests of vocabulary and grammatical knowledge. The majority of children are verbal but impaired in language, relative to age-matched peers. One hypothesis is that this subgroup has ASD and co-morbid specific language impairment (SLI). An experiment was conducted comparing children with ASD to children with SLI and typically developing controls on aspects of language processing that have been shown to be impaired in children with SLI: repetition of nonsense words. Patterns of performance among the children with ASD and language impairment were similar to those with SLI, and contrasted with the children with ASD and no language impairment and typical controls, providing further evidence for the hypothesis that a subgroup of children with ASD has co-morbid SLI. The findings are discussed in the context of brain imaging studies that have explored the neural bases of language impairment in ASD and SLI, and overlap in the genes associated with elevated risk for these disorders.M01 RR00533 - NCRR NIH HHS; R01 DC10290 - NIDCD NIH HHS; U19 DC03610 - NIDCD NIH HH

Risk Adjustment In Neurocritical care (RAIN)--prospective validation of risk prediction models for adult patients with acute traumatic brain injury to use to evaluate the optimum location and comparative costs of neurocritical care: a cohort study.

OBJECTIVES: To validate risk prediction models for acute traumatic brain injury (TBI) and to use the best model to evaluate the optimum location and comparative costs of neurocritical care in the NHS. DESIGN: Cohort study. SETTING: Sixty-seven adult critical care units. PARTICIPANTS: Adult patients admitted to critical care following actual/suspected TBI with a Glasgow Coma Scale (GCS) score of < 15. INTERVENTIONS: Critical care delivered in a dedicated neurocritical care unit, a combined neuro/general critical care unit within a neuroscience centre or a general critical care unit outside a neuroscience centre. MAIN OUTCOME MEASURES: Mortality, Glasgow Outcome Scale - Extended (GOSE) questionnaire and European Quality of Life-5 Dimensions, 3-level version (EQ-5D-3L) questionnaire at 6 months following TBI. RESULTS: The final Risk Adjustment In Neurocritical care (RAIN) study data set contained 3626 admissions. After exclusions, 3210 patients with acute TBI were included. Overall follow-up rate at 6 months was 81%. Of 3210 patients, 101 (3.1%) had no GCS score recorded and 134 (4.2%) had a last pre-sedation GCS score of 15, resulting in 2975 patients for analysis. The most common causes of TBI were road traffic accidents (RTAs) (33%), falls (47%) and assault (12%). Patients were predominantly young (mean age 45 years overall) and male (76% overall). Six-month mortality was 22% for RTAs, 32% for falls and 17% for assault. Of survivors at 6 months with a known GOSE category, 44% had severe disability, 30% moderate disability and 26% made a good recovery. Overall, 61% of patients with known outcome had an unfavourable outcome (death or severe disability) at 6 months. Between 35% and 70% of survivors reported problems across the five domains of the EQ-5D-3L. Of the 10 risk models selected for validation, the best discrimination overall was from the International Mission for Prognosis and Analysis of Clinical Trials in TBI Lab model (IMPACT) (c-index 0.779 for mortality, 0.713 for unfavourable outcome). The model was well calibrated for 6-month mortality but substantially underpredicted the risk of unfavourable outcome at 6 months. Baseline patient characteristics were similar between dedicated neurocritical care units and combined neuro/general critical care units. In lifetime cost-effectiveness analysis, dedicated neurocritical care units had higher mean lifetime quality-adjusted life-years (QALYs) at small additional mean costs with an incremental cost-effectiveness ratio (ICER) of £14,000 per QALY and incremental net monetary benefit (INB) of £17,000. The cost-effectiveness acceptability curve suggested that the probability that dedicated compared with combined neurocritical care units are cost-effective is around 60%. There were substantial differences in case mix between the 'early' (within 18 hours of presentation) and 'no or late' (after 24 hours) transfer groups. After adjustment, the 'early' transfer group reported higher lifetime QALYs at an additional cost with an ICER of £11,000 and INB of £17,000. CONCLUSIONS: The risk models demonstrated sufficient statistical performance to support their use in research but fell below the level required to guide individual patient decision-making. The results suggest that management in a dedicated neurocritical care unit may be cost-effective compared with a combined neuro/general critical care unit (although there is considerable statistical uncertainty) and support current recommendations that all patients with severe TBI would benefit from transfer to a neurosciences centre, regardless of the need for surgery. We recommend further research to improve risk prediction models; consider alternative approaches for handling unobserved confounding; better understand long-term outcomes and alternative pathways of care; and explore equity of access to postcritical care support for patients following acute TBI. FUNDING: The National Institute for Health Research Health Technology Assessment programme

Hydrogenase biomimetics with redox-active ligands: Electrocatalytic proton reduction by [Fe2(CO)4(κ2-diamine)(μ-edt)] (diamine = 2,2′-bipy, 1,10-phen)

Diiron complexes bearing redox active diamine ligands have been studied as models of the active site of [FeFe]-hydrogenases. Heating [Fe2(CO)6(μ-edt)] (edt = 1,2-ethanedithiolate) with 2,2′-bipyridine (2,2′-bipy) or 1,10-phenanthroline (1,10-phen) in MeCN in the presence of Me3NO leads to the formation of [Fe2(CO)4(κ2-2,2′-bipy)(μ-edt)] (1-edt) and [Fe2(CO)4(κ2-1,10-phen)(μ-edt)] (2-edt), respectively, in moderate yields. In the solid state the diamine resides in dibasal sites, while both dibasal and apical–basal isomers are present in solution. Both stereoisomers protonate readily upon addition of strong acids. Cyclic voltammetry in MeCN shows that both complexes undergo irreversible oxidation and reduction, proposed to be a one- and two-electron process, respectively. The structures of neutral 2-edt and its corresponding one- and two-electron reduced species have been investigated by DFT calculations. In 2-edt− the added electron occupies a predominantly ligand-based orbital, and the iron–iron bond is maintained, being only slightly elongated. Addition of the second electron affords an open-shell triplet dianion where the second electron populates an Fe–Fe σ* antibonding orbital, resulting in effective scission of the iron–iron bond. The triplet state lies 4.2 kcal mol−1 lower in energy than the closed-shell singlet dianion whose HOMO correlates nicely with the LUMO of the neutral species 2-edt. Electrocatalytic proton reduction by both complexes has been studied in MeCN using CF3CO2H as the proton source. These catalysis studies reveal that while at high acid concentrations the active catalytic species is [Fe2(CO)4(μ-H)(κ2-diamine)(μ-edt)]+, at low acid concentrations the two complexes follow different catalytic mechanisms being associated with differences in their relative rates of protonation

Fichte and Hegel on Recognition

In this paper I provide an interpretation of Hegel’s account of ‘recognition’ (Anerkennung) in the 1802-3 System of Ethical Life as a critique of Fichte’s account of recognition in the 1796-7 Foundations of Natural Right. In the first three sections of the paper I argue that Fichte’s account of recognition in the domain of right is not concerned with recognition as a moral attitude. I then turn, in section four, to a discussion of Hegel’s critique and transformation of Fichte’s conception of recognition. Hegel’s transformation consists, I argue, in the claim that a comprehensive account of recognition in the domain of right must be concerned with recognition as a moral attitude