A robust approach to model-based classification based on trimming and constraints

In a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the test set. Therefore, unreliable labelled observations, namely outliers and data with incorrect labels, can strongly undermine the classifier performance, especially if the training size is small. The present work introduces a robust modification to the Model-Based Classification framework, employing impartial trimming and constraints on the ratio between the maximum and the minimum eigenvalue of the group scatter matrices. The proposed method effectively handles noise presence in both response and exploratory variables, providing reliable classification even when dealing with contaminated datasets. A robust information criterion is proposed for model selection. Experiments on real and simulated data, artificially adulterated, are provided to underline the benefits of the proposed method

Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.Comment: To be published in IEEE Communications Surveys and Tutorial

Apprentissage automatique pour le codage cognitif de la parole

Depuis les années 80, les codecs vocaux reposent sur des stratégies de codage à court terme qui fonctionnent au niveau de la sous-trame ou de la trame (généralement 5 à 20 ms). Les chercheurs ont essentiellement ajusté et combiné un nombre limité de technologies disponibles (transformation, prédiction linéaire, quantification) et de stratégies (suivi de forme d'onde, mise en forme du bruit) pour construire des architectures de codage de plus en plus complexes. Dans cette thèse, plutôt que de s'appuyer sur des stratégies de codage à court terme, nous développons un cadre alternatif pour la compression de la parole en codant les attributs de la parole qui sont des caractéristiques perceptuellement importantes des signaux vocaux. Afin d'atteindre cet objectif, nous résolvons trois problèmes de complexité croissante, à savoir la classification, la prédiction et l'apprentissage des représentations. La classification est un élément courant dans les conceptions de codecs modernes. Dans un premier temps, nous concevons un classifieur pour identifier les émotions, qui sont parmi les attributs à long terme les plus complexes de la parole. Dans une deuxième étape, nous concevons un prédicteur d'échantillon de parole, qui est un autre élément commun dans les conceptions de codecs modernes, pour mettre en évidence les avantages du traitement du signal de parole à long terme et non linéaire. Ensuite, nous explorons les variables latentes, un espace de représentations de la parole, pour coder les attributs de la parole à court et à long terme. Enfin, nous proposons un réseau décodeur pour synthétiser les signaux de parole à partir de ces représentations, ce qui constitue notre dernière étape vers la construction d'une méthode complète de compression de la parole basée sur l'apprentissage automatique de bout en bout. Bien que chaque étape de développement proposée dans cette thèse puisse faire partie d'un codec à elle seule, chaque étape fournit également des informations et une base pour la prochaine étape de développement jusqu'à ce qu'un codec entièrement basé sur l'apprentissage automatique soit atteint. Les deux premières étapes, la classification et la prédiction, fournissent de nouveaux outils qui pourraient remplacer et améliorer des éléments des codecs existants. Dans la première étape, nous utilisons une combinaison de modèle source-filtre et de machine à état liquide (LSM), pour démontrer que les caractéristiques liées aux émotions peuvent être facilement extraites et classées à l'aide d'un simple classificateur. Dans la deuxième étape, un seul réseau de bout en bout utilisant une longue mémoire à court terme (LSTM) est utilisé pour produire des trames vocales avec une qualité subjective élevée pour les applications de masquage de perte de paquets (PLC). Dans les dernières étapes, nous nous appuyons sur les résultats des étapes précédentes pour concevoir un codec entièrement basé sur l'apprentissage automatique. un réseau d'encodage, formulé à l'aide d'un réseau neuronal profond (DNN) et entraîné sur plusieurs bases de données publiques, extrait et encode les représentations de la parole en utilisant la prédiction dans un espace latent. Une approche d'apprentissage non supervisé basée sur plusieurs principes de cognition est proposée pour extraire des représentations à partir de trames de parole courtes et longues en utilisant l'information mutuelle et la perte contrastive. La capacité de ces représentations apprises à capturer divers attributs de la parole à court et à long terme est démontrée. Enfin, une structure de décodage est proposée pour synthétiser des signaux de parole à partir de ces représentations. L'entraînement contradictoire est utilisé comme une approximation des mesures subjectives de la qualité de la parole afin de synthétiser des échantillons de parole à consonance naturelle. La haute qualité perceptuelle de la parole synthétisée ainsi obtenue prouve que les représentations extraites sont efficaces pour préserver toutes sortes d'attributs de la parole et donc qu'une méthode de compression complète est démontrée avec l'approche proposée.Abstract: Since the 80s, speech codecs have relied on short-term coding strategies that operate at the subframe or frame level (typically 5 to 20ms). Researchers essentially adjusted and combined a limited number of available technologies (transform, linear prediction, quantization) and strategies (waveform matching, noise shaping) to build increasingly complex coding architectures. In this thesis, rather than relying on short-term coding strategies, we develop an alternative framework for speech compression by encoding speech attributes that are perceptually important characteristics of speech signals. In order to achieve this objective, we solve three problems of increasing complexity, namely classification, prediction and representation learning. Classification is a common element in modern codec designs. In a first step, we design a classifier to identify emotions, which are among the most complex long-term speech attributes. In a second step, we design a speech sample predictor, which is another common element in modern codec designs, to highlight the benefits of long-term and non-linear speech signal processing. Then, we explore latent variables, a space of speech representations, to encode both short-term and long-term speech attributes. Lastly, we propose a decoder network to synthesize speech signals from these representations, which constitutes our final step towards building a complete, end-to-end machine-learning based speech compression method. The first two steps, classification and prediction, provide new tools that could replace and improve elements of existing codecs. In the first step, we use a combination of source-filter model and liquid state machine (LSM), to demonstrate that features related to emotions can be easily extracted and classified using a simple classifier. In the second step, a single end-to-end network using long short-term memory (LSTM) is shown to produce speech frames with high subjective quality for packet loss concealment (PLC) applications. In the last steps, we build upon the results of previous steps to design a fully machine learning-based codec. An encoder network, formulated using a deep neural network (DNN) and trained on multiple public databases, extracts and encodes speech representations using prediction in a latent space. An unsupervised learning approach based on several principles of cognition is proposed to extract representations from both short and long frames of data using mutual information and contrastive loss. The ability of these learned representations to capture various short- and long-term speech attributes is demonstrated. Finally, a decoder structure is proposed to synthesize speech signals from these representations. Adversarial training is used as an approximation to subjective speech quality measures in order to synthesize natural-sounding speech samples. The high perceptual quality of synthesized speech thus achieved proves that the extracted representations are efficient at preserving all sorts of speech attributes and therefore that a complete compression method is demonstrated with the proposed approach

School-based interventions for reducing disciplinary school exclusion: a systematic review.

UNLABELLED: This Campbell systematic review examines the impact of interventions to reduce exclusion from school. School exclusion, also known as suspension in some countries, is a disciplinary sanction imposed by a responsible school authority, in reaction to students' misbehaviour. Exclusion entails the removal of pupils from regular teaching for a period during which they are not allowed to be present in the classroom (in-school) or on school premises (out-of-school). In some extreme cases the student is not allowed to come back to the same school (expulsion). The review summarises findings from 37 reports covering nine different types of intervention. Most studies were from the USA, and the remainder from the UK. Included studies evaluated school-based interventions or school-supported interventions to reduce the rates of exclusion. Interventions were implemented in mainstream schools and targeted school-aged children from four to 18, irrespective of nationality or social background. Only randomised controlled trials are included. The evidence base covers 37 studies. Thirty-three studies were from the USA, three from the UK, and for one study the country was not clear. School-based interventions cause a small and significant drop in exclusion rates during the first six months after intervention (on average), but this effect is not sustained. Interventions seemed to be more effective at reducing some types of exclusion such as expulsion and in-school exclusion. Four intervention types - enhancement of academic skills, counselling, mentoring/monitoring, and skills training for teachers - had significant desirable effects on exclusion. However, the number of studies in each case is low, so this result needs to be treated with caution. There is no impact of the interventions on antisocial behaviour. Variations in effect sizes are not explained by participants' characteristics, the theoretical basis of the interventions, or the quality of the intervention. Independent evaluator teams reported lower effect sizes than research teams who were also involved in the design and/or delivery of the intervention. PLAIN LANGUAGE SUMMARY: Interventions can reduce school exclusion but the effect is temporary: Some interventions - enhancement of academic skills, counselling, mentoring/monitoring, and skills training for teachers - appear to have significant effects on exclusion.The review in brief: Interventions to reduce school exclusion are intended to mitigate the adverse effects of this school sanction. Some approaches, namely those involving enhancement of academic skills, counselling, mentoring/monitoring and those targeting skills training for teachers, have a temporary effect in reducing exclusion. More evaluations are needed to identify the most effective types of intervention; and whether similar effects are also found in different countries.What is this review about?: School exclusion is associated with undesirable effects on developmental outcomes. It increases the likelihood of poor academic performance, antisocial behavior, and poor employment prospects. This school sanction disproportionally affects males, ethnic minorities, those who come from disadvantaged economic backgrounds, and those with special educational needs.This review assesses the effectiveness of programmes to reduce the prevalence of exclusion.What are the main findings of this review?: What studies are included? Included studies evaluated school-based interventions or school-supported interventions to reduce the rates of exclusion. Interventions were implemented in mainstream schools and targeted school-aged children from four to 18, irrespective of nationality or social background. Only randomised controlled trials are included.The evidence base covers 37 studies. Thirty-three studies were from the USA, three from the UK, and for one study the country was not clear.School-based interventions cause a small and significant drop in exclusion rates during the first six months after intervention (on average), but this effect is not sustained. Interventions seemed to be more effective at reducing some types of exclusion such as expulsion and in-school exclusion.Four intervention types - enhancement of academic skills, counselling, mentoring/ monitoring, and skills training for teachers - had significant desirable effects on exclusion. However, the number of studies in each case is low, so this result needs to be treated with caution.There is no impact of the interventions on antisocial behaviour.Variations in effect sizes are not explained by participants' characteristics, the theoretical basis of the interventions, or the quality of the intervention. Independent evaluator teams reported lower effect sizes than research teams who were also involved in the design and/or delivery of the intervention.What do the findings of this review mean?: School-based interventions are effective at reducing school exclusion immediately after, and for a few months after, the intervention (6 months on average). Four interventions presented promising and significant results in reducing exclusion, that is, enhancement of academic skills, counselling, mentoring/monitoring, skills training for teachers. However, since the number of studies for each sub-type of intervention was low, we suggest these results should be treated with caution.Most of the studies come from the USA. Evaluations are needed from other countries in which exclusion is common. Further research should take advantage of the possibility of conducting cluster-randomised controlled trials, whilst ensuring that the sample size is sufficiently large.How up-to-date is this review?: The review authors searched for studies published up to December 2015. This Campbell systematic review was published in January 2018. EXECUTIVE SUMMARY/ABSTRACT: BACKGROUND: Schools are important institutions of formal social control (Maimon, Antonaccio, & French, 2012). They are, apart from families, the primary social system in which individuals are socialised to follow specific codes of conduct. Violating these codes of conduct may result in some form of punishment. School punishment is normally accepted by families and students as a consequence of transgression, and in that sense school isoften the place where children are first introduced to discipline, justice, or injustice (Whitford & Levine-Donnerstein, 2014).A wide range of punishments may be used in schools, from verbal reprimands to more serious actions such as detention, fixed term exclusion or even permanent exclusion from the mainstream education system. It must be said that in some way, these school sanctions resemble the penal system and its array of alternatives to punish those that break the law.School exclusion, also known as suspension in some countries, is defined as a disciplinary sanction imposed by a responsible school authority, in reaction to students' misbehaviour. Exclusion entails the removal of pupils from regular teaching for a period during which they are not allowed to be present in the classroom or, in more serious cases, on school premises.Based on the previous definition, this review uses school exclusion and school suspension as synonyms, unless the contrary is explicitly stated. Most of the available research has found that exclusion correlates with subsequent negative sequels on developmental outcomes. Exclusion or suspension of students is associated with failure within the academic curriculum, aggravated antisocial behaviour, and an increased likelihood of involvement with punitive social control institutions (i.e., the Juvenile Justice System). In the long-term, opportunities for training and employment seem to be considerably reduced for those who have repeatedly been excluded. In addition to these negative correlated outcomes, previous evidence suggest that the exclusion of students involves a high economic cost for taxpayers and society.Research from the last 20 years has concluded quite consistently that this disciplinary measure disproportionally targets males, ethnic minorities, those who come from disadvantaged economic backgrounds, and those presenting special educational needs. In other words, suspension affects the most vulnerable children in schools.Different programmes have attempted to reduce the prevalence of exclusion. Although some of them have shown promising results, so far, no comprehensive systematic review has examined these programmes' overall effectiveness.OBJECTIVES: The main goal of the present research is to systematically examine the available evidence for the effectiveness of different types of school-based interventions aimed at reducing disciplinary school exclusion. Secondary goals include comparing different approaches and identifying those that could potentially demonstrate larger and more significant effects.The research questions underlying this project are as follows: Do school-based programmes reduce the use of exclusionary sanctions in schools?Are some school-based approaches more effective than others in reducing exclusionary sanctions?Do participants' characteristics (e.g., age, gender, ethnicity) affect the impact of school-based programmes on exclusionary sanctions in schools?Do characteristics of the interventions, implementation, and methodology affect the impact of school-based programmes on exclusionary sanctions in schools? SEARCH METHODS: The authors conducted a comprehensive search to locate relevant studies reporting on the impact of school-based interventions on exclusion from 1980 onwards. Twenty-seven different databases were consulted, including databases that contained both published and unpublished literature. In addition, we contacted researchers in the field of school-exclusion for further recommendations of relevant studies; we also assessed citation lists from previous systematic and narrative reviews and research reports. Searches were conducted from September 1 to December 1, 2015.SELECTION CRITERIA: The inclusion and exclusion criteria for manuscripts were defined before we started our searches. To be eligible, studies needed to have: evaluated school-based interventions or school-supported interventions intended to reduce the rates of suspension; seen the interventions as an alternative to exclusion; targeted school-aged children from four to 18 in mainstream schools irrespective of nationality or social background; and reported results of interventions delivered from 1980 onwards. In terms of methodological design, we included randomised controlled trialsonly, with at least one experimental group and onecontrol or placebo group.DATA COLLECTION AND ANALYSIS: Initial searches produced a total of 42,749 references from 27 different electronic databases. After screening the title, abstract and key words, we kept 1,474 relevant hits. 22 additional manuscripts were identified through other sources (e.g., assessment of citation lists, contribution of authors). After removing duplicates, we ended up with a total of 517 manuscripts. Two independent coders evaluated each report, to determine inclusion or exclusion.The second round of evaluation excluded 472 papers, with eight papers awaiting classification, and 37 studies kept for inclusion in meta-analysis. Two independent evaluators assessed all the included manuscripts for risk of quality bias by using EPOC tool.Due to the broad scope of our targeted programmes, meta-analysis was conducted under a random-effect model. We report the impact of the intervention using standardised differences of means, 95% confidence intervals along with the respective forest plots. Sub-group analysis and meta-regression were used for examining the impact of the programme. Funnel plots and Duval and Tweedie's trim-and-fill analysis were used to explore the effect of publication bias.RESULTS: Based on our findings, interventions settled in school can produce a small and significant drop in exclusion rates (SMD=.30; 95% CI .20 to .41; p<.001). This means that those participating in interventions are less likely to be suspended than those allocated to control/placebo groups. These results are based on measures of impact collected immediately during the first six months after treatment (on average). When the impact was tested in the long-term (i.e., 12 or more months after treatment), the effects of the interventions were not sustained. In fact, there was a substantive reduction in the impact of school-based programmes (SMD=.15; 95%CI -.06 to .35), and it was no longer statistically significant.We ran analysis testing the impact of school-based interventions on different types of exclusion. Evidence suggests that interventions are more effective at reducing expulsion and in-school exclusion than out-of-school exclusion. In fact, the impact of intervention in out-of-school exclusion was close to zero and not statistically significant.Nine different types of school-based interventions were identified across the 37 studies included in the review. Four of them presented favourable and significant results in reducing exclusion (i.e., enhancement of academic skills, counselling, mentoring/monitoring, skills training for teachers). Since the number of studies for each sub-type of intervention was low, we suggest that results should be treated with caution.A priori defined moderators (i.e., participants' characteristics, the theoretical basis of the interventions, and quality of the intervention)showed not to be effective at explaining the heterogeneity present in our results. Among three post-hoc moderators, the role of the evaluator was found to be significant: independent evaluator teams reported lower effect sizes than research teams who were also involved in the design and/or delivery of the intervention.Two researchers independently evaluated the quality of the evidence involved in this review by using the EPOC tool. Most of the studies did not present enough information for the judgement of quality bias.AUTHORS' CONCLUSIONS: The evidence suggests that school-based interventions are effective at reducing school exclusion immediately after, and for a few months after, the intervention. Some specific types of interventions show more promising and stable results than others, namely those involving mentoring/monitoring and those targeting skills training for teachers. However, based on the number of studies involved in our calculations, we suggest that results must be cautiously interpreted. Implications for policy and practice arising from our results are discussed

Electroanalgesia: Historical and Contemporary Developments

Aims and Objectives: This thesis makes an in-depth examination of the historical, including the eighteenth-century pioneering electrical treatments of the Rev John Wesley, together with contemporary developments in electroanalgesia from the late twentieth-century, including the author's own pilot study, in order to provide a sound, scientific basis for their continuing use. The problem and the hypothesis: Controversy still surrounds the effectiveness of electrical treatments, even after 250 years of application. This is seen in its most researched form as TENS (transcutaneous electrical nerve stimulation) and ALTENS (acupuncture-like transcu taneous electrical nerve stimulation) for chronic back pain. The empirical research making up the main part of the thesis sets out to provide clear evidence to reject the null hypothesis, i.e. that there are no significant clinical effects from the use of electrical treatments for chronic back pain. Methods and findings: The empirical tertiary research centred on a systematic review and meta-analysis, within the framework of the Cochrane Collaboration, of all randomised controlled trials of TENS/ ALTENS for chronic back pain found during rigorous searches of the medical literature. Pooling their results in a meta-analysis established that effective clinical benefits are to be found in the use of ALTENS/TENS for chronic back pain, at least in the short term. Conclusions and recommendations: This wide ranging PhD thesis demonstrates for the first time significant clinical benefits of TENS/ ALTENS for treating patients with chronic back pain and if implemented on a global basis, then considerable numbers of chronic back pain sufferers could benefit

Textile Fingerprinting for Dismount Analysis in the Visible, Near, and Shortwave Infrared Domain

The ability to accurately and quickly locate an individual, or a dismount, is useful in a variety of situations and environments. A dismount\u27s characteristics such as their gender, height, weight, build, and ethnicity could be used as discriminating factors. Hyperspectral imaging (HSI) is widely used in efforts to identify materials based on their spectral signatures. More specifically, HSI has been used for skin and clothing classification and detection. The ability to detect textiles (clothing) provides a discriminating factor that can aid in a more comprehensive detection of dismounts. This thesis demonstrates the application of several feature selection methods (i.e., support vector machines with recursive feature reduction, fast correlation based filter) in highly dimensional data collected from a spectroradiometer. The classification of the data is accomplished with the selected features and artificial neural networks. A model for uniquely identifying (fingerprinting) textiles are designed, where color and composition are determined in order to fingerprint a specific textile. An artificial neural network is created based on the knowledge of the textile\u27s color and composition, providing a uniquely identifying fingerprinting of a textile. Results show 100% accuracy for color and composition classification, and 98% accuracy for the overall textile fingerprinting process

Development and assessment of evidence-based strategies towards increased feasibility and transparency of investigator-initiated clinical trials in Switzerland

This work addresses the obligation to minimize research waste by identifying barriers and needs for support in important processes of clinical research and by proposing efficient strategies to improve the quality of research practice. Major sources of waste in clinical research have been identified by the “Increasing Value, Reducing Waste” series in The Lancet in 2014. Two considerations in this series address the problem of inefficient trial management and insufficient research transparency. Collected evidence suggests that inefficient management and monitoring of the procedural conduct of trials are a major source of waste even in well-designed studies addressing important questions. The absence of a continuous oversight of established trial processes endanger completion of trials in a set timeframe or even cause premature discontinuation. Increasing feasibility of clinical trials by providing an evidence-based strategy to effectively support the conduct of clinical trials at the University Hospital of Basel that has the potential to be transferred to the whole academic network for clinical research in Switzerland was aspired in this thesis. Along with feasibility, it is important that information of a trial including results is publicly available. In Switzerland, prospective registration of a clinical trial in a primary trial registry has been made mandatory by law in 2014 (Art 56 Human Research Act). We analyzed research transparency in terms of trial registration and results publication in a local setting in Switzerland to assess the successful implementation and enforcement of national efforts and identify potential barriers. In a first step, we systematically reviewed existing evidence on effective monitoring strategies both in the medical literature and across international clinical research stakeholder groups. Monitoring strategies varied in their methodological approach but the effectiveness of risk-based and triggered approaches could be shown with moderate certainty. However, we did not find evidence on the effect of these methods on the overall trial conduct. Based on these findings, we then engaged local, national and international stakeholder representatives in the creation of a comprehensive risk-tailored approach integrating monitoring in the broader context of trial management. We systematically reviewed information on risk indicators commonly used to guide monitoring in the academic setting and in industry and identified risk elements extended to the overall management of a clinical trial. In order to continuously visualize the status of identified risk elements throughout the study conduct, we initiated the user-centered development of a supporting study dashboard. The final risk-tailored approach consisted of the following components: A study-specific risk assessment prior to study start, selection and development of data based pathways addressing the identified risks, and the continuous visualization of the status of risk elements in a study dashboard. The generic content of the dashboard provides continuous information and support for risk indicators applicable to almost all clinical trials (Data quality, Recruitment, Retention, and Safety management) and the optional content is based on further study-specific items identified during the risk assessment (e.g. Follow-up visits, Re-consent process, Sampling management, Imaging quality). User-testing of the risk assessment and study dashboards developed on the basis of the assessment revealed that the continuous oversight of most critical elements and support of managing these elements efficiently supports the work routine of principle investigators, trial managers and trial monitors. In a second project of this thesis, we assessed current trial registration and publication for clinical intervention studies approved by the Ethics Committee North and Central Switzerland (EKNZ) in the last five years. Registration of all clinical trials would provide an overview of what research is being conducted at present and registries constitute an ideal platform for the publication and dissemination of research results.. Identifying factors influencing registration and potential barriers provides a basis for further initiatives to increase trial registration. Prospective trial registration has increased over the last five years and trials with higher risk category, multicenter trials and trials taking advantage of Clinical Trials Unit services were associated with higher registration rates. Although prospective trial registration prevalence has improved within the last five years within the EKNZ approved studies, a strong need for support in the registration process was identified in our qualitative evaluation. The impact of this work - and whether it eventually increases feasibility and transparency in clinical research critically depends on its implementation, evaluation, and refinement. Sharing current knowledge on effective monitoring strategies with trialists and monitors to choose evidence-based strategies for their trials constitutes a major support for investigator-initiated trials in the academic environment. The advancement of a risk-based trial monitoring approach into a comprehensive risk-tailored approach supporting the overall conduct of a trial and considering trial monitoring as an integrative part of trial management has the potential to efficiently optimize study processes. While an uptake of the study specific risk assessment and the use of a study dashboard as a standard process would be aspired for all RCTs in the future, improving the timeline and resources needed for the development of a study specific dashboard will be important to advance the generation of affordable and efficient dashboards for investigator-initiated trials. Sharing evidence on the registration behavior and perceived barriers by researchers in the local setting of the EKNZ helps to understand underlying processes and test measures for improvement. Supporting researchers in the process of trial registration and educating research institutes and investigators about the need and advantages of trial registration, has the potential to facilitate the implementation of automated processes and SOPs ensuring the registration of all clinical trials. Establishing trial registries as a primary platform for sharing research results should be aspired in the future

Psychological preparation and postoperative outcomes for adults undergoing surgery under general anaesthesia

Acknowledgements We wish to dedicate this work to the memory of Christian Osmer, a dedicated, caring doctor who was committed to achieving the best care for his patients and their relatives. He saw his contribution to this project as a way of advancing best care for surgical patients. We are very grateful for his valuable input to this work and the pleasure we had in working with him. We are grateful to Karen Hovhanisyan (former Trials Search Co-ordinator, Cochrane Anaesthesia, Critical and Emergency Care Group (ACE)) for carrying out the electronic database searches and to Jane Cracknell (Managing Editor, ACE) for her support throughout the review process. We would also like to thank W Alastair Chambers and Manjeet Shehmar for clinical advice relating to judgements about general anaesthesia usage, and Yvonne Cooper and Louise Pike who retrieved documents and screened papers as research assistants in earlier stages of the review. We are grateful to the following colleagues who helped us with foreign language papers - either by screening papers or by providing translation: Stefano Carrubba, Chuan Gao, Chen Ji, Kate Rhie, Reza Roudsari and Alena Vasianovich. We would like to thank Andy Smith (content editor), Nathan Pace (statistical editor), Michael Donnelly, Allan Cyna and Michael Wang (peer reviewers), and Shunjie Chua (consumer referee) for their help and editorial advice during the preparation of this systematic review. We would also like to thank Andrew Smith (content editor), Nathan Pace (statistical editor), Michael Wang and Allan Cyna (peer reviewers), and Lynda Lane (Cochrane Consumer Network representative) for their help and editorial advice during the preparation of the protocol (Powell 2010). Sources of support Internal sources Manchester Centre for Health Psychology, University of Manchester, UK. An award of £2000 was received to support research assistant costs. External sources British Academy, UK. We received a small research grant of £7480 to support research assistant costs.Peer reviewedPublisher PD