The choice between hip prosthetic bearing surfaces in total hip replacement:a protocol for a systematic review and network meta-analysis

BACKGROUND: Prosthetic hip implants have many combinations of bearing surface materials, sizes, and fixation techniques, which can determine the quality of life of patients after primary total hip replacement (THR) and the likelihood of needing revision surgery. When an implant fails, patients require revision THR, which is distressing to the patient and expensive for the health care payer. Primary THR is one of the most common elective procedures performed worldwide, with over 300,000 performed annually in the USA and over 80,000 in England and Wales. It is important to review all available randomised controlled trial (RCT) evidence to determine which implant bearing surface materials, size, and fixation technique are most effective for patients. METHODS/DESIGN: This is a protocol for a systematic review and meta-analysis of RCTs comparing outcomes of hip implant bearing surfaces, size, and fixation techniques used in THR. Implant combinations compared in the literature include four bearing surface combinations (metal-on-polyethylene, metal-on-metal, ceramic-on-polyethylene, and ceramic-on-ceramic); two femoral head sizes (large vs small heads); and four fixation techniques (uncemented, cemented, hybrid, and reverse hybrids). The primary outcome will be revision surgery. We will also collect data on patient characteristics, mortality, quality of life, and other outcomes. In network meta-analysis, we will estimate the relative effectiveness of every implant bearing surface, head size (large vs small), and fixation permutation, using evidence where implants have been compared directly in an RCT and indirectly through common comparators in different RCTs. DISCUSSION: There has been much debate about materials used for prosthetic implants in THR. Different combinations of prosthetic materials, sizes, and fixation, can vary widely in cost and fail at different rates for different patient groups. Given the number of THRs performed yearly, and the increasing use of expensive implants, it is important to review evidence to inform surgeons, patients, and health care providers of optimal implant bearing combinations for given patient characteristics. This review will inform a cost-effectiveness model that will include evidence from other sources, to determine the most effective and cost-effective implant bearing combination for patients. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD4201501943

On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure

Maximising response to postal questionnaires – A systematic review of randomised trials in health research

Background Postal self-completion questionnaires offer one of the least expensive modes of collecting patient based outcomes in health care research. The purpose of this review is to assess the efficacy of methods of increasing response to postal questionnaires in health care studies on patient populations. Methods The following databases were searched: Medline, Embase, CENTRAL, CDSR, PsycINFO, NRR and ZETOC. Reference lists of relevant reviews and relevant journals were hand searched. Inclusion criteria were randomised trials of strategies to improve questionnaire response in health care research on patient populations. Response rate was defined as the percentage of questionnaires returned after all follow-up efforts. Study quality was assessed by two independent reviewers. The Mantel-Haenszel method was used to calculate the pooled odds ratios. Results Thirteen studies reporting fifteen trials were included. Implementation of reminder letters and telephone contact had the most significant effect on response rates (odds ratio 3.7, 95% confidence interval 2.30 to 5.97 p = <0.00001). Shorter questionnaires also improved response rates to a lesser degree (odds ratio 1.4, 95% confidence interval 1.19 to 1.54). No evidence was found that incentives, re-ordering of questions or including an information brochure with the questionnaire confer any additional advantage. Conclusion Implementing repeat mailing strategies and/or telephone reminders may improve response to postal questionnaires in health care research. Making the questionnaire shorter may also improve response rates. There is a lack of evidence to suggest that incentives are useful. In the context of health care research all strategies to improve response to postal questionnaires require further evaluation

Behavioral Economic Measurement of Cigarette Demand: A Descriptive Review of Published Approaches to the Cigarette Purchase Task

The cigarette purchase task (CPT) is a behavioral economic method for assessing demand for cigarettes. Growing interest in behavioral correlates of tobacco use in clinical and general populations as well as empirical efforts to inform policy has seen an increase in published articles employing the CPT. Accordingly, an examination of the published methods and procedures for obtaining these behavioral economic metrics is timely. The purpose of this investigation was to provide a review of published approaches to using the CPT. We searched specific Boolean operators ([“behavioral economic” AND “purchase task”] OR [“demand” AND “cigarette”]) and identified 49 empirical articles published through the year 2018 that reported administering a CPT. Articles were coded for participant characteristics (e.g., sample size, population type, age), CPT task structure (e.g., price framing, number and sequence of prices; vignettes, contextual factors), and data analytic approach (e.g., method of generating indices of cigarette demand). Results of this review indicate no standard approach to administering the CPT and underscore the need for replicability of these behavioral economic measures for the purpose of guiding clinical and policy decisions

Childhood socioeconomic position and objectively measured physical capability levels in adulthood: a systematic review and meta-analysis

&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; Grip strength, walking speed, chair rising and standing balance time are objective measures of physical capability that characterise current health and predict survival in older populations. Socioeconomic position (SEP) in childhood may influence the peak level of physical capability achieved in early adulthood, thereby affecting levels in later adulthood. We have undertaken a systematic review with meta-analyses to test the hypothesis that adverse childhood SEP is associated with lower levels of objectively measured physical capability in adulthood.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methods and Findings:&lt;/b&gt; Relevant studies published by May 2010 were identified through literature searches using EMBASE and MEDLINE. Unpublished results were obtained from study investigators. Results were provided by all study investigators in a standard format and pooled using random-effects meta-analyses. 19 studies were included in the review. Total sample sizes in meta-analyses ranged from N = 17,215 for chair rise time to N = 1,061,855 for grip strength. Although heterogeneity was detected, there was consistent evidence in age adjusted models that lower childhood SEP was associated with modest reductions in physical capability levels in adulthood: comparing the lowest with the highest childhood SEP there was a reduction in grip strength of 0.13 standard deviations (95% CI: 0.06, 0.21), a reduction in mean walking speed of 0.07 m/s (0.05, 0.10), an increase in mean chair rise time of 6% (4%, 8%) and an odds ratio of an inability to balance for 5s of 1.26 (1.02, 1.55). Adjustment for the potential mediating factors, adult SEP and body size attenuated associations greatly. However, despite this attenuation, for walking speed and chair rise time, there was still evidence of moderate associations.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; Policies targeting socioeconomic inequalities in childhood may have additional benefits in promoting the maintenance of independence in later life.&lt;/p&gt

Efficacy and safety of non-steroidal anti-inflammatory drugs (NSAIDs) for the treatment of acute pain after orthopedic trauma: a practice management guideline from the Eastern Association for the Surgery of Trauma and the Orthopedic Trauma Association

OBJECTIVES: Fracture is a common injury after a traumatic event. The efficacy and safety of non-steroidal anti-inflammatory drugs (NSAIDs) to treat acute pain related to fractures is not well established. METHODS: Clinically relevant questions were determined regarding NSAID use in the setting of trauma-induced fractures with clearly defined patient populations, interventions, comparisons and appropriately selected outcomes (PICO). These questions centered around efficacy (pain control, reduction in opioid use) and safety (non-union, kidney injury). A systematic review including literature search and meta-analysis was performed, and the quality of evidence was graded per the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. The working group reached consensus on the final evidence-based recommendations. RESULTS: A total of 19 studies were identified for analysis. Not all outcomes identified as critically important were reported in all studies, and the outcome of pain control was too heterogenous to perform a meta-analysis. Nine studies reported on non-union (three randomized control trials), six of which reported no association with NSAIDs. The overall incidence of non-union in patients receiving NSAIDs compared with patients not receiving NSAIDs was 2.99% and 2.19% (p=0.04), respectively. Of studies reporting on pain control and reduction of opioids, the use of NSAIDs reduced pain and the need for opioids after traumatic fracture. One study reported on the outcome of acute kidney injury and found no association with NSAID use. CONCLUSIONS: In patients with traumatic fractures, NSAIDs appear to reduce post-trauma pain, reduce the need for opioids and have a small effect on non-union. We conditionally recommend the use of NSAIDs in patients suffering from traumatic fractures as the benefit appears to outweigh the small potential risks

Conducting robust ecological analyses with climate data

Although the number of studies discerning the impact of climate change on ecological systems continues to increase, there has been relatively little sharing of the lessons learnt when accumulating this evidence. At a recent workshop entitled ‘Using climate data in ecological research’ held at the UK Met Office, ecologists and climate scientists came together to discuss the robust analysis of climate data in ecology. The discussions identified three common pitfalls encountered by ecologists: 1) selection of inappropriate spatial resolutions for analysis; 2) improper use of publically available data or code; and 3) insufficient representation of the uncertainties behind the adopted approach. Here, we discuss how these pitfalls can be avoided, before suggesting ways that both ecology and climate science can move forward. Our main recommendation is that ecologists and climate scientists collaborate more closely, on grant proposals and scientific publications, and informally through online media and workshops. More sharing of data and code (e.g. via online repositories), lessons and guidance would help to reconcile differing approaches to the robust handling of data. We call on ecologists to think critically about which aspects of the climate are relevant to their study system, and to acknowledge and actively explore uncertainty in all types of climate data. And we call on climate scientists to make simple estimates of uncertainty available to the wider research community. Through steps such as these, we will improve our ability to robustly attribute observed ecological changes to climate or other factors, while providing the sort of influential, comprehensive analyses that efforts to mitigate and adapt to climate change so urgently require

ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions

Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I ("Risk Of Bias In Non-randomised Studies-of Interventions"), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies