Evaluation Protocol Research Articles

Development and Validation of an Examination Protocol for Arthroscopic Evaluation of the Temporomandibular Joint in Dogs.

Due to the previously limited intra-articular diagnostic possibilities of the canine temporomandibular joint, an examination protocol for the canine temporomandibular joint (TMJ) was developed and tested in this study using a needle arthroscope. In total, the discotemporal (DTJ) and discomandibular (DMJ) joint compartments of 32 animals (64 TMJs) were examined arthroscopically. During the examinations, 15 anatomical landmarks per joint side were evaluated in regard to their visibility and accessibility. All arthroscopies were performed by the same examiner and the same assistant to ensure standard methods were applied. The examination procedure which was developed here proved to be a reliable tool for examining this joint. The 15 anatomical landmarks that were to be examined could be reliably visualised and assessed in all TMJs with a certainty of 86% to 100% by both observers. This tool provides clinicians with a reliable examination aid for everyday practice and ensures the comparability of results. In the future, this could provide an opportunity to better diagnose and treat TMJ pathologies.

Fundamentals of Arthroscopic Surgery Training and beyond: a reinforcement learning exploration and benchmark

PurposeThis work presents FASTRL, a benchmark set of instrument manipulation tasks adapted to the domain of reinforcement learning and used in simulated surgical training. This benchmark enables and supports the design and training of human-centric reinforcement learning agents which assist and evaluate human trainees in surgical practice.MethodsSimulation tasks from the Fundamentals of Arthroscopic Surgery Training (FAST) program are adapted to the reinforcement learning setting for the purpose of training virtual agents that are capable of providing assistance and scoring to the surgical trainees. A skill performance assessment protocol is presented based on the trained virtual agents.ResultsThe proposed benchmark suite presents an API for training reinforcement learning agents in the context of arthroscopic skill training. The evaluation scheme based on both heuristic and learned reward functions robustly recovers the ground truth ranking on a diverse test set of human trajectories.ConclusionThe presented benchmark enables the exploration of a novel reinforcement learning-based approach to skill performance assessment and in-procedure assistance for simulated surgical training scenarios. The evaluation protocol based on the learned reward model demonstrates potential for evaluating the performance of surgical trainees in simulation.

Measurement-Based Care in Youth: An Opportunity for Better Clinical Outcomes?

Measurement-based care (MBC) is a procedure in which systematic and routine assessments are performed. Through this practice, clinicians can verify the progress of the symptomatology of the patient and adapt the appointments and the intervention to the current symptoms. Studies have reflected on the importance and the benefits of this type of procedure in the adult population, and have shown positive results. However, there is a lack of evidence concerning the remaining populations. Regarding youth, for instance, few articles have evaluated the benefits of using this procedure in clinical practice. However, research focused on this topic has revealed positive results, especially when clinicians were loyal to the MBC procedures. Still, further research is needed. This letter aims to share the methodology used by our multidisciplinary team, composed of psychologists and psychiatrists, in a clinical context at the Hospital Cruz Vermelha, Lisboa, applied to the adult population; the objective is to share and discuss some alterations that could be made to our evaluation protocol to enable the same to be used with the youth population. We believe that implementing MBC for youth is crucial for several reasons, including enhanced treatment efficacy, more personalized treatment, a reduced reliance on subjectivity, and empowerment not only of patients but also families.

Adaptation of the technology readiness levels for impact assessment in implementation sciences: The TRL-IS checklist

BackgroundIntervention development is a critical process in implementation research. There are key stages involved in the process to design, pilot, demonstrate and release a technology or an intervention. The Technology Readiness Level (TRL) is a globally accepted instrument for assessing the maturity of research development. However, the original levels do not fit all, and some adjustments are required for its applicability in implementation sciences. AimsThis study aimed to gather the prior knowledge base on TRL in public and population health research; to develop a standard definition of readiness, and to adapt and validate the TRL to an implementation science context (TRL-IS). Materials and methodsA Mixed methods approach has been followed in this study. A scoping review using the PRISMA extension (PRISMA-ScR) informed a nominal expert panel for developing a standard definition of readiness and to modify the TRL following an ontoterminology approach. Then the maturity of six practical case study examples were rated by ten researchers using the modified TRL to estimate inter-rater reliability, and a group of experts provided final content and face validity and feasibility.This mixed methods study included 1) a scoping review to examine the current literature and develop a knowledge base, identify knowledge gaps and to clarify concepts; 2) the development of a standard definition of ‘Readiness’ and related terms; and 3) adaptation of the TRL to implementation science and development of a checklist to rate the maturity of applications.A standard definition of readiness and related terms was produced by the core team, and an international nominal group (n = 30) was conducted to discuss and validate the definition and terms, and the location of ‘Readiness’ in the initiation and early development phases of implementation.Following feedback from the nominal group, the development of the TRL-IS was finalised and a TRL-IS rating checklist was developed to rate the maturity of applications. The TRL-IS checklist was tested using six cases based on real world studies on implementation research.The inter-rater reliability of the TRL-IS was evaluated by ten raters and finally six raters evaluated the content and face validity, and feasibility, of the TRL-IS checklist using the System Usability Scale (SUS). ResultsFew papers (n = 11) utilised the TRL to evaluate the readiness of readiness of health and social science implementation research. The main changes in the adaption of the TRL-IS included the removal of laboratory testing, limiting the use of “operational” environment and a clearer distinction between level 6 (pilot in a relevant environment) and 7 (demonstration in the real world prior to release). The adapted version was considered relevant by the expert panel. The TRL-IS checklist showed evidence of good inter-rater reliability (ICC = 0.90 with 95 % confident interval = 0.74–0.98, p < .001) and provides a consistent metric. ConclusionsIn spite of recommendations made by national and international research funding agencies, few health and social science implementation studies include the TRL as part of their evaluation protocol. The TRL-IS offers a high degree of conceptual clarity between scientific maturity phases or readiness levels, and good reliability among raters of varying experience. This study highlights that adoption of the TRL-IS framework in implementation sciences will bolster the scientific robustness and comparability of research maturity in this domain.

Oxidative stress, redox status and surfactant metabolism in mechanically ventilated patients receiving different approaches to oxygen therapy (MecROX): An observational study protocol for mechanistic evaluation

Background MecROX is a mechanistic sub-study of the UK-ROX trial which was designed to evaluate the clinical and cost-effectiveness of a conservative approach to oxygen therapy for invasively ventilated adults in intensive care. This is based on the scientific rationale that excess oxygen is harmful. Epithelial cell damage with alveolar surfactant deficiency is characteristic of hyperoxic acute lung injury. Additionally, hyperoxaemia (excess blood oxygen levels) may exacerbate whole-body oxidative stress leading to cell death, autophagy, mitochondrial dysfunction, bioenergetic failure and multi-organ failure resulting in poor clinical outcomes. However, there is a lack of in-vivo human models evaluating the mechanisms that underpin oxygen-induced organ damage in mechanically ventilated patients. Aim The aim of the MecROX mechanistic sub-study is to assess lung surfactant composition and global systemic redox status to provide a mechanistic and complementary scientific rationale to the UK-ROX trial findings. The objectives are to quantify in-vivo surfactant composition, synthesis, and metabolism with markers of oxidative stress and systemic redox disequilibrium (as evidenced by alterations in the ‘reactive species interactome’) to differentiate between groups of conservative and usual oxygen targets. Methods and design After randomisation into the UK-ROX trial, 100 adult participants (50 in the conservative and 50 in usual care group) will be recruited at two trial sites. Blood and endotracheal samples will be taken at 0, 48 and 72 hours following an infusion of 3 mg/kg methyl-D9-choline chloride. This is a non-radioactive, stable isotope of choline (vitamin), which has been extensively used to study surfactant phospholipid kinetics in humans. This study will mechanistically evaluate the in-vivo surfactant synthesis and breakdown (by hydrolysis and oxidation), oxidative stress and redox disequilibrium from sequential plasma and bronchial samples using an array of analytical platforms. We will compare conservative and usual oxygenation groups according to the amount of oxygen administered. Trial registration: ISRCTN ISRCTN61929838, 27/03/2023 https://doi.org/10.1186/ISRCTN61929838.

Usability Analysis of Smart Speakers from a Learnability Perspective for Novel Users

Although commercial smart speakers are becoming increasingly popular, there is still much potential for investigation into their usability. In this study, we analyzed the usability of commercial smart speakers by focusing on the learnability of young users who are not yet familiar with voice user interface (VUI) operation. In the experiment, we conducted a task in which users repeatedly operated a smart speaker 10 times under four conditions, combining two experimental factors: the presence or absence of a screen on the smart speaker and the operation method (voice control only or in conjunction with remote-control operation). The usability of the smart speaker was analyzed in terms of task-completion time, task-completion rate, number of errors, subjective evaluation, and retrospective protocol analysis. In particular, we confirmed and compared the learning curves for each condition in terms of the performance metrics. The experimental results showed that there were no substantial differences in the learning curves between the presence and absence of a screen. In addition, the “lack of feedback” and “system response error” were identified as usability problems, and it was suggested that these problems led to “distrust of the system”.

From MNIST to ImageNet and back: benchmarking continual curriculum learning

Continual learning (CL) is one of the most promising trends in recent machine learning research. Its goal is to go beyond classical assumptions in machine learning and develop models and learning strategies that present high robustness in dynamic environments. This goal is realized by designing strategies that simultaneously foster the incorporation of new knowledge while avoiding forgetting past knowledge. The landscape of CL research is fragmented into several learning evaluation protocols, comprising different learning tasks, datasets, and evaluation metrics. Additionally, the benchmarks adopted so far are still distant from the complexity of real-world scenarios, and are usually tailored to highlight capabilities specific to certain strategies. In such a landscape, it is hard to clearly and objectively assess models and strategies. In this work, we fill this gap for CL on image data by introducing two novel CL benchmarks that involve multiple heterogeneous tasks from six image datasets, with varying levels of complexity and quality. Our aim is to fairly evaluate current state-of-the-art CL strategies on a common ground that is closer to complex real-world scenarios. We additionally structure our benchmarks so that tasks are presented in increasing and decreasing order of complexity—according to a curriculum—in order to evaluate if current CL models are able to exploit structure across tasks. We devote particular emphasis to providing the CL community with a rigorous and reproducible evaluation protocol for measuring the ability of a model to generalize and not to forget while learning. Furthermore, we provide an extensive experimental evaluation showing that popular CL strategies, when challenged with our proposed benchmarks, yield sub-par performance, high levels of forgetting, and present a limited ability to effectively leverage curriculum task ordering. We believe that these results highlight the need for rigorous comparisons in future CL works as well as pave the way to design new CL strategies that are able to deal with more complex scenarios.

Fatores correlacionados a Síndrome de Burnout entre acadêmicos: Estudo Transversal

Despite being predominant in the workplace, Burnout Syndrome (BS) is becoming more and more frequent in the university environment. With the objective of verifying the prevalence of BS among students and correlating this variable with sociodemographic and health aspects, this cross-sectional study was conducted with a sample of 72 participants.The evaluation protocol consisted of a sociodemographic questionnaire, evaluation of Burnout Syndrome, depressive symptoms, general health, social aspects, and musculoskeletal symptoms. Spearman's correlation was used for statistical analysis. There was a predominance of BS among the sample (65.2%). Significant correlations were observed between social aspects and disbelief (p=0.016; r=-0.283), student efficacy (p=&lt;0.001; r=-0.399) and depressive symptoms (p=&lt;0.001; r=-0.547), as well as between general health and depressive symptoms (p=0.012; r=-0.295). Establishing this type of measure allows planning actions aimed at student assistance, prioritizing interventions and preventive practices that collaborate with health promotion in the university environment.

The Development of Computer Assisted Vocabulary Learning (CAVL) to Improve English Lexical Retention of Nursing Students

Vocabulary mastery is crucial for nursing students to effectively communicate in English. This study aimed to develop and validate a tailored computer-assisted vocabulary learning (CAVL) intervention to improve nursing students' retention of English lexicon, which is essential for healthcare communications. The CAVL program was designed using the Moodle platform and focused on four thematic units that targeted essential nursing vocabulary. The learning process followed research-based principles of vocabulary instruction, including multimodal introduction, reinforced retrieval, and contextual repetition. This study utilized a research and development methodology to conduct iterative needs analyses, design refinement, and rigorous evaluation protocols. Expert reviews, prototype testing, post-intervention vocabulary tests, and delayed assessments were used to gather data and make data-driven improvements. Quantitative analysis evaluated the effectiveness of this approach for 100 Indonesian nursing students. Vocabulary assessments were administered before, immediately after, and two weeks after the intervention, revealing significant improvements in terminology knowledge following the implementation of CAVL. Importantly, scores remained stable during the delayed assessment, demonstrating durable retention. These results are consistent with previous literature on the benefits of contextual and multimodal vocabulary learning. The CAVL prototype facilitated learning and effectively improved vocabulary retention outside of the classroom. This research provides an adaptable framework for technologically-assisted language mastery, which is essential for the next generation of nursing education. Further studies can explore the application of this framework in allied healthcare fields and the transition to practice in nursing education.

Validation of the Equidyn protocol for evaluation of dynamic balance in older adults through a smartphone application

BackgroundDifferent tasks and proxy measurements have been employed to evaluate dynamic balance in older individuals. However, due to inherent limitations, results from most evaluations could hardly be taken as valid measurements of dynamic balance. Research questionIs the Equidyn smartphone application-based protocol valid and sensitive for assessment of dynamic balance in older adults? MethodsDynamic balance was evaluated in 52 physically active individuals, age range 60–80 years (M = 69.36). The dynamic tasks were one-leg sway either in the mediolateral (ML) or anteroposterior (AP) direction while supported on the contralateral leg, and cyclic sit-to-stand with a narrow support base. These tasks were performed under standardized movement amplitude and rhythm. Outcomes were correlated with unipedal quiet standing. A smartphone was attached to the trunk backside, and a custom-made application (Equidyn) was employed to provide guidance throughout evaluation, timed beeps to pace the movements, and three-dimensional trunk acceleration measurement for balance evaluation. ResultsOur data showed (a) that both ML and AP leg sway tasks were sensitive to aging and to direction of leg sway movements; (b) referenced to quiet unipedal stance, moderate/strong correlations for the ML/AP leg sway tasks and moderate correlations for the sit-to-stand task; and (c) moderate/strong correlations between the ML and AP leg sway tasks, and moderate correlations between the sit-to-stand and the two unipedal dynamic tasks in the ML acceleration direction. SignificanceThe current results support the conclusion that the Equidyn protocol is a sensitive and valid tool to evaluate dynamic balance in healthy older individuals. The protocol tasks standardized in amplitude and rhythm favor their reproducibility and trunk acceleration data interpretation. As the whole assessment is made through a smartphone application, this dynamic balance evaluation could be made in a low-cost simple way both in the laboratory and clinical settings.

In vitro evaluation of doxorubicin release from diopside particles on MG-63 and HF spheroids as a 3D model of tumor and healthy tissues

Local drug delivery systems based on bioceramics ensure safe and effective treatment of bone defects and anticancer therapy. A promising drug delivery scaffold material for bone treatment applications is diopside (CaMgSi2O6) which is bioactive, degradable, and possesses drug-release ability. Currently, in vitro assessment of drug release from biomaterials is performed mostly on a 2D cell monolayer. However, to interpret and integrate biochemical signals, cells need a 3D microenvironment that provides cell-cell and cell-extracellular matrix interactions. In this regard, 3D cell models are gaining popularity. In this work, we proposed the protocol for evaluation of the effect of doxorubicin released from diopside on MG-63 cells and primary human fibroblasts in 3D culture conditions. Tissue spheroids with similar diameters were incubated with doxorubicin-loaded diopside for 72 h, the amount of diopside was calculated in accordance with the required doxorubicin concentration. We demonstrated that doxorubicin is gradually released from diopside and exhibits an activity similar to that of the pure drug at the same total concentration. It is important to note that doxorubicin was more potent on MG-63 spheroids compared to HF spheroids, which confirmed the reliability of spheroids as 3D models of tumor and healthy tissues.

Evaluating the occurrence and spatial patterns of soil water repellency in the Deschutes National Forest, Oregon

AbstractHigh levels of soil water repellency (SWR) can hinder water infiltration and increase surface soil erosion risk and runoff. Although SWR occurs naturally in many areas, it is often patchy and does not impede water movement. However, fire can increase the connectedness and extent of SWR leading to topsoil loss, nutrient limitations, increased root water stress, and ultimately slower ecosystem recovery. This study examines the naturally hydrophobic soils of Oregon's Deschutes National Forest following the 2020 Green Ridge Fire to (1) examine the relationship between SWR and management‐relevant burn severity classes, (2) quantify an appropriate spatial scale over which to evaluate SWR properties, and (3) determine which environmental factors drive patterns in SWR. We found that the top 1–3 cm of soil became less hydrophobic after fire, while the profile to about 10 cm became more uniformly hydrophobic. This could indicate that surface soil is more prone to post‐fire erosion in burned areas. However, predicting SWR is still challenging. While burn severity and environmental metrics did statistically account for some variation in SWR following fire, the range of SWR spatial autocorrelation was at best a few meters. Due to this fine‐scale variation, future work should focus on determining an efficient post‐fire soil evaluation protocol with adequate density and scale of sampling while also incorporating the influence of environmental factors to inform management decisions.

Study Protocol of Implementation and Evaluation of Aging Home Modification Intervention Program (AhMIP) for Iranian Community-Dwelling Older Adults

Introduction With the rapid increase in the older population in Iran, the housing needs of this age group are also changing. The residential environment plays an important role in the ability of older adults to perform daily activities. This study will be conducted to implement and evaluate a home modification intervention program to support older adults. Method This paper describes the protocol for a triangulation mixed methods design study with three substudies. The first substudy is a cross-sectional study to be conducted on 422 community-dwelling older adults to assess their current status at home, functional abilities, fear of falling, and quality of life. In the second substudy, a randomized clinical intervention trial will be conducted on 116 older adults (58 intervention group and 58 control group) with the intervention of home modifications based on the results of the Home Falls and Accidents Screening Tool. The third substudy is a qualitative study with an in-depth, semi-structured interview approach to find out about older adults’ experienecs regarding AhMIP. Conclusion The quantitative and qualitative findings of this study can be useful in designing intervention programs to improve the living environment of older Iranian adults and promote their quality of life.

A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges

Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency limitations of black-box models. Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs. We analysed the SoA to provide a taxonomy, a uniform notation, and the benchmarking datasets and evaluation metrics. We discuss fourteen methods, their evaluation protocols, twenty-two datasets, and nineteen metrics. We integrated the majority of methods into the GRETEL library to conduct an empirical evaluation to understand their strengths and pitfalls. We highlight open challenges and future work.

Rethink data-driven human behavior prediction: A Psychology-powered Explainable Neural Network

Human behavior prediction is a rapidly evolving field with numerous applications in various domains. The rapid development of machine learning facilitates research on data-driven behavior prediction. At the same time, in human factors domains, researchers still focus on modeling psychological decision-making processes from the psychological perspectives of view based on subjective data. However, it is difficult to obtain one’s psychological states in real-world scenarios, given that the collection of subjective data in real-world settings is difficult and intrusive. Thus, psychology theories can hardly be applied to behavior prediction directly. To bridge the gap between data-driven behavior prediction and psychological decision-making models, we proposed a novel Psychology-powered Explainable Neural network (PEN). In PEN, for the first time, the psychological factors of individuals (i.e., human-side features such as attitudes to technologies) are modeled and recovered explicitly based on one’s historical behaviors in certain scenarios. The multi-task optimization ensures each component in PEN to be optimized with proper gradient. Assessment results over three evaluation protocols demonstrate that, based on the historical behaviors of an individual only, the PEN can outperform the best existing behavior prediction models by 1.87% and 4.68% on two datasets when a 1:9 training–testing data partition was adopted. The framework in PEN has provided a new solution for explainable human behavior prediction when psychological information is not directly available.

Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study

BackgroundThis research focused on the development of a motor imagery (MI) based brain–machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will.MethodsA total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants’ neural activity using the second deep learning approach for the decoding.ResultsThe three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance.ConclusionThis research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study’s discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

Morphological Differentiation of Corneal Inflammatory Cells: Proposal of Pragmatic Protocol.

Corneal confocal microscopy is a noninvasive imaging technique to analyze corneal nerve fibers and corneal inflammatory cells (CICs). The amount of CICs is a potential biomarker of disease activity in chronic autoinflammatory diseases. To date, there are no standardized criteria for the morphological characterization of CICs. The aim was to establish a protocol for a standardized morphological classification of CICs based on a literature search and to test this protocol for applicability and reliability. A systematic review of the literature about definitions of CICs was conducted. Existing morphological descriptions were translated into a structured algorithm and applied by raters. Subsequently, the protocol was optimized by reducing and defining the criteria of the cell types. The optimized algorithm was applied by 4 raters. The interrater reliability was calculated using Fleiss kappa (K). A systematic review of the literature revealed no uniform morphological criteria for the differentiation of the individual cell types in CICs. Our first protocol achieved only a low level of agreement between 3 raters (K = 0.09; 1062 rated cells). Our revised protocol was able to achieve a higher interrater reliability with 3 (K = 0.64; 471 rated cells) and 4 (K = 0.61; 628 rated cells) raters. The indirect use of criteria from the literature leads to a high error rate. By clearly defining the individual cell types and standardizing the protocol, reproducible results were obtained, allowing the introduction of this protocol for the future evaluation of CICs in the corneal confocal microscopy.

PR-PL: A Novel Prototypical Representation Based Pairwise Learning Framework for Emotion Recognition Using EEG Signals

Affective brain-computer interface based on electroencephalography (EEG) is an important branch in the field of affective computing. However, the individual differences in EEG emotional data and the noisy labeling problem in the subjective feedback seriously limit the effectiveness and generalizability of existing models. To tackle these two critical issues, we propose a novel transfer learning framework with Prototypical Representation based Pairwise Learning ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PR-PL</i> ). The discriminative and generalized EEG features are learned for emotion revealing across individuals and the emotion recognition task is formulated as pairwise learning for improving the model tolerance to the noisy labels. More specifically, a prototypical learning is developed to encode the inherent emotion-related semantic structure of EEG data and align the individuals' EEG features to a shared common feature space under consideration of the feature separability of both source and target domains. Based on the aligned feature representations, pairwise learning with an adaptive pseudo labeling method is introduced to encode the proximity relationships among samples and alleviate the label noises effect on modeling. Extensive results on two benchmark databases (SEED and SEED-IV) under four different cross-validation evaluation protocols validate the model reliability and stability across subjects and sessions. Compared to the literature, the average enhancement of emotion recognition across four different evaluation protocols is 2.04% (SEED) and 2.58% (SEED-IV). The source code is available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/KAZABANA/PR-PL</uri></i> .

Cost-effectiveness of a radio intervention to stimulate early childhood development: protocol for an economic evaluation of the SUNRISE trial in Burkina Faso

IntroductionApproximately 250 million children under 5 years of age are at risk of poor development in low-income and middle-income countries. However, existing early childhood development (ECD) interventions can be expensive,...

Alpha 2 agonists for sedation to produce better outcomes from critical illness (A2B trial): protocol for a mixed-methods process evaluation of a randomised controlled trial

IntroductionAn association between deep sedation and adverse short-term outcomes has been demonstrated although this evidence has been inconsistent. The A2B (alpha-2 agonists for sedation in critical care) sedation trial is...