Background Identify and establish consensus regarding potential prognostic factors for the development of chronic pain after a first episode of idiopathic, non-traumatic neck pain. Design This study used two consensus group methods: a modified Nominal Group (m-NGT) and a Delphi Technique. Methods The goal of the m-NGT was to obtain and categorize a list of potential modifiable prognostic factors. These factors were presented to a multidisciplinary panel in a two-round Delphi survey, which was conducted between November 2018 and January 2020. The participants were asked whether factors identified are of prognostic value, whether these factors are modifiable, and how to measure these factors in clinical practice. Consensus was a priori defined as 70% agreement among participants. Results Eighty-four factors were identified and grouped into seven categories during the expert meeting using the modified NGT. A workgroup reduced the list to 47 factors and grouped them into 12 categories. Of these factors, 26 were found to be potentially prognostic for chronification of neck pain (> 70% agreement). Twenty-one out of these 26 factors were found to be potentially modifiable by physiotherapists based on a two-round Delphi survey. Conclusion Based on an expert meeting (m-NGT) and a two-round Delphi survey, our study documents consensus (> 70%) on 26 prognostic factors. Twenty-one out of these 26 factors were found to be modifiable, and most factors were psychological in nature.
In this work, the concept of an Artificial Intelligence-based (AI) Digital Twin (DT) of an aircraft system is introduced, with the goal to improve the corresponding MRO Operations. More specifically, the current study aims to obtaining knowledge on the optimal placement of sensors in an ideal Power Electronics Cooling System (PECS) of a modern airliner, aiming to improve input data as a basis for an AI-based DT. The three main fluid parameters to be measured directly or indirectly at various physical locations at the PECS are mass flow rate, temperature and static pressure. The physics-based model can then be combined with a Machine Learning (ML) model, such as a Random Forest (RF), with a multitude of decision trees. Following, the AI system determines whether the PECS operations is considered normal, aiming to optimize the performance of the system and to maximize the Useful Remaining Life (URL). The suggested AI-DT approach is based both on data-driven and physics-based models, an approach which results in increased reliability and availability, reducing possible Aircraft on Ground (AOG) events. Subsequently, the enhanced prediction capability results in the optimization of the maintenance processes and in reduced operational costs.
It is important for caregivers and patients to know which wounds are at risk of prolonged wound healing to enable timely communication and treatment. Available prognostic models predict wound healing in chronic ulcers, but not in acute wounds, that is, originating after trauma or surgery. We developed a model to detect which factors can predict (prolonged) healing of complex acute wounds in patients treated in a large wound expertise centre (WEC). Using Cox and linear regression analyses, we determined which patient- and wound-related characteristics best predict time to complete wound healing and derived a prediction formula to estimate how long this may take. We selected 563 patients with acute wounds, documented in the WEC registry between 2007 and 2012. Wounds had existed for a median of 19 days (range 6-46 days). The majority of these were located on the leg (52%). Five significant independent predictors of prolonged wound healing were identified: wound location on the trunk [hazard ratio (HR) 0·565, 95% confidence interval (CI) 0·405-0·788; P = 0·001], wound infection (HR 0·728, 95% CI 0·534-0·991; P = 0·044), wound size (HR 0·993, 95% CI 0·988-0·997; P = 0·001), wound duration (HR 0·998, 95% CI 0·996-0·999; P = 0·005) and patient's age (HR 1·009, 95% CI 1·001-1·018; P = 0·020), but not diabetes. Awareness of the five factors predicting the healing of complex acute wounds, particularly wound infection and location on the trunk, may help caregivers to predict wound healing time and to detect, refer and focus on patients who need additional attention.
