1,721,004 research outputs found
Prognostic factors for chronic headache – a systematic review
No full textObjective: To identify predictors of prognosis and trial outcomes in prospective studies of people with chronic headache.Methods: This was a systematic review of published literature in peer-reviewed journals. We included (1) randomized controlled trials (RCTs) of interventions for chronic headache that reported subgroup analyses and (2) prospective cohort studies, published in English, since 1980. Participants included adults with chronic headache (including chronic headache, chronic migraine, and chronic tension-type headache with or without medication overuse headache). We searched key databases using free text and MeSH terms. Two reviewers independently extracted data and assessed the methodologic quality of studies and overall quality of evidence identified using appropriate published checklists.Results: We identified 16,556 titles, removed 663 duplicates, and reviewed 199 articles, of which 27 were included in the review—17 prospective cohorts and 10 RCTs with subgroup analyses reported. There was moderate-quality evidence indicating that depression, anxiety, poor sleep and stress, medication overuse, and poor self-efficacy for managing headaches are potential prognostic factors for poor prognosis and unfavorable outcomes from preventive treatment in chronic headache. There was inconclusive evidence about treatment expectations, age, age at onset, body mass index, employment, and several headache features.Conclusions: This review identified several potential predictors of poor prognosis and worse outcome postinterventions in people with chronic headache. The majority of these are modifiable. The findings also highlight the need for more longitudinal high-quality research of prognostic factors in chronic headache.<br/
The effects of supported employment interventions in populations of people with conditions other than severe mental health : a systematic review
Full text linkTo assess the effectiveness of supported employment interventions for improving competitive employment in populations of people with conditions other than only severe mental illness. Supported employment interventions have been extensively tested in severe mental illness populations. These approaches may be beneficial outside of these populations. We searched PubMed, Embase, CINAHL, PsycInfo, Web of Science, Scopus, JSTOR, PEDro, OTSeeker, and NIOSHTIC for trials including unemployed people with any condition and including severe mental illness if combined with other co-morbidities or other specific circumstances (e.g., homelessness). We excluded trials where inclusion was based on severe mental illness alone. Two reviewers independently assessed risk of bias (RoB v2.0) and four reviewers extracted data. We assessed rates of competitive employment as compared to traditional vocational rehabilitation or waiting list/services as usual. Ten randomised controlled trials (913 participants) were included. Supported employment was more effective than control interventions for improving competitive employment in seven trials: in people with affective disorders [risk ratio (RR) 10.61 (1.49, 75.38)]; mental disorders and justice involvement [RR 4.44 (1.36,14.46)]; veterans with posttraumatic stress disorder (PTSD) [RR 2.73 (1.64, 4.54)]; formerly incarcerated veterans [RR 2.17 (1.09, 4.33)]; people receiving methadone treatment [RR 11.5 (1.62, 81.8)]; veterans with spinal cord injury at 12 months [RR 2.46 (1.16, 5.22)] and at 24 months [RR 2.81 (1.98, 7.37)]; and young people not in employment, education, or training [RR 5.90 (1.91-18.19)]. Three trials did not show significant benefits from supported employment: populations of workers with musculoskeletal injuries [RR 1.38 (1.00, 1.89)]; substance abuse [RR 1.85 (0.65, 5.41)]; and formerly homeless people with mental illness [RR 1.55 (0.76, 3.15)]. Supported employment interventions may be beneficial to people from more diverse populations than those with severe mental illness alone. Defining competitive employment and increasing (and standardising) measurement of non-vocational outcomes may help to improve research in this area
Identification of subgroup effect with an individual participant data meta-analysis of randomised controlled trials of three different types of therapist-delivered care in low back pain
Full text linkBackground:
Proven treatments for low back pain, at best, only provide modest overall benefits. Matching people to treatments that are likely to be most effective for them may improve clinical outcomes and makes better use of health care resources.
Methods:
We conducted an individual participant data meta-analysis of randomised controlled trials of three types of therapist delivered interventions for low back pain (active physical, passive physical and psychological treatments). We applied two statistical methods (recursive partitioning and adaptive risk group refinement) to identify potential subgroups who might gain greater benefits from different treatments from our individual participant data meta-analysis.
Results:
We pooled data from 19 randomised controlled trials, totalling 9328 participants. There were 5349 (57%) females with similar ratios of females in control and intervention arms. The average age was 49 years (standard deviation, SD, 14).
Participants: with greater psychological distress and physical disability gained most benefit in improving on the mental component scale (MCS) of SF-12/36 from passive physical treatment than non-active usual care (treatment effects, 4.3; 95% confidence interval, CI, 3.39 to 5.15). Recursive partitioning method found that participants with worse disability at baseline gained most benefit in improving the disability (Roland Morris Disability Questionnaire) outcome from psychological treatment than non-active usual care (treatment effects, 1.7; 95% CI, 1.1 to 2.31). Adaptive risk group refinement did not find any subgroup that would gain much treatment effect between psychological and non-active usual care. Neither statistical method identified any subgroups who would gain an additional benefit from active physical treatment compared to non-active usual care.
Conclusions:
Our methodological approaches worked well and may have applicability in other clinical areas. Passive physical treatments were most likely to help people who were younger with higher levels of disability and low levels of psychological distress. Psychological treatments were more likely to help those with severe disability. Despite this, the clinical importance of identifying these subgroups is limited. The sizes of sub-groups more likely to benefit and the additional effect sizes observed are small. Our analyses provide no evidence to support the use of sub-grouping for people with low back pain
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Recursive partitioning based approaches for low back pain subgroup identification in individual patient data meta-analyses
Full text linkThis thesis presents two novel approaches for performing subgroup analyses or identifying subgroups in an individual patient data (IPD) meta-analyses setting. The work contained in this thesis originated from an important research priority in the area of low back pain (LBP); identifying subgroups that most (or least) benefit from treatment. Typically, a subgroup is evaluated by applying a statistical test for interaction between a baseline characteristic and treatment. A systematic review found that subgroup analyses in the area of LBP are severely underpowered and are of a rather poor quality (Chapter 4). IPD meta-analyses provide an ideal framework with improved statistical power to investigate and identify subgroups. However, conventional approaches to subgroup analyses applied in both a single trial setting and an IPD setting have a number of issues, one of them being that subgroups are typically investigated one at a time. As individuals have multiple characteristics that may be related to response to treatment, alternative statistical methods are required to overcome the associated issues. Tree based methods are a promising alternative that systematically search the entire covariate space to identify subgroups defined by multiple characteristics. In this work, a number of relevant tree methods, namely the Interaction Tree (IT), Simultaneous Threshold Interaction Modelling Algorithm (STIMA) and Subpopulation Identification based on a Differential Effect Search (SIDES), were identified and evaluated in a single trial setting in a simulation study. The most promising methods (IT and SIDES) were extended for application in an IPD meta-analyses setting by incorporating fixed-effect and mixed-effect models to account for the within trial clustering in the hierarchical data structure, and again assessed in a simulation study. Thus, this work proposes two statistical approaches to subgroup analyses or subgroup identification in an IPD meta-analysis framework. Though the application is based in a LBP setting, the extensions are applicable in any research discipline where subgroup analyses in an IPD meta-analysis setting is of interest
Non-pharmacological self-management for people living with migraine or tension-type headache:a systematic review including analysis of intervention components
Full text linkObjectivesTo assess the effect of non-pharmacological self-management interventions against usual care, and to explore different components and delivery methods within those interventionsParticipantsPeople living with migraine and/or tension-type headacheInterventionsNon-pharmacological educational or psychological self-management interventions; excluding biofeedback and physical therapy. We assessed the overall effectiveness against usual care on headache frequency, pain intensity, mood, headache related disability, quality of life, and medication consumption in meta-analysis. We also provide preliminary evidence on the effectiveness of intervention components and delivery methods.Results We found a small overall effect for the superiority of self-management interventions over usual care, with a SMD of-0.36 (-0.45 to -0.26) for pain intensity; -0.32 (-0.42 to -0.22) for headache related disability, 0.32 (0.20 to 0.45) for quality of life and a moderate effect on mood (SMD = 0.53 (-0.66 to -0.40)). We did not find an effect on headache frequency (SMD = -0.07 (-0.22 to 0.08). Assessment of components and characteristics suggests a larger effects on pain intensity in interventions that included explicit educational components (-0.51 (-0.68 to -0.34) versus -0.28 (-0.40 to -0.16)); mindfulness components (-0.50 (-0.82 to -0.18) versus 0.34 (-0.44 to -0.24) and in interventions delivered in groups versus one-to-one delivery (0.56 (-0.72 to -0.40) versus -0.39 (-0.52 to -0.27) and larger effects on mood in interventions including a CBT component with a SMD of -0.72 (-0.93 to -0.51) compared to those without CBT -0.41 (-0.58 to -0.24). Conclusion Overall we found that self-management interventions for migraine and tension-type headache are more effective than usual care in reducing pain intensity, mood, and headache related disability. Preliminary findings also suggest that including CBT, mindfulness and educational components in interventions, and delivery in groups may increase effectiveness.RegistrationPROSPERO 2016:CRD4201604129
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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