The QUality of Interhospital Transportation in the Euregion Meuse-Rhine (QUIT-EMR) score: a cross-validation study

ObjectivesInterhospital transports of critically ill patients are high-risk medical interventions. Well-established parameters to quantify the quality of transports are currently lacking. We aimed to develop and cross-validate a score for interhospital transports.SettingAn expert panel developed a score for interhospital transport by a Mobile Intensive Care Unit (MICU), the QUality of Interhospital Transportation in the Euregion Meuse-Rhine (QUIT-EMR) score. The QUIT-EMR score is an overall sum score that includes component scores of monitoring and intervention variables of the neurological (proxy for airway patency), respiratory and circulatory organ systems, ranging from −12 to +12. A score of 0 or higher defines an adequate transport. The QUIT-EMR score was tested to help to quantify the quality of transport.ParticipantsOne hundred adult patients were randomly included and the transport charts were independently reviewed and classified as adequate or inadequate by four transport experts (ie, anaesthetists/intensivists).Outcome measuresSubsequently, the level of agreement between the QUIT-EMR score and expert classification was calculated using Gwet’s AC1.ResultsFrom April 2012 to May 2014, a total of 100 MICU transports were studied. The median (IQR) QUIT-EMR score was 1 (0–2). Experts classified six transports as inadequate. The percentage agreement between the QUIT-EMR score and experts’ classification for adequate/inadequate transport ranged from 84% to 92% (Gwet’s AC10.81–0.91). The interobserver agreement between experts was 87% to 94% (Gwet’s AC10.89–0.98).ConclusionThe QUIT-EMR score is a novel validated tool to score MICU transportation adequacy in future studies contributing to quality control and improvement.Trial registration numberNTR 4937.

Development of Monitoring Robot System for Tomato Fruits in Hydroponic Greenhouses

Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep learning algorithms are increasing in the development of agricultural robots since deep learning algorithms that use convolutional neural networks have been proven to show outstanding performance in image classification, segmentation, and object detection. However, most of these applications are focused on the development of harvesting robots, and thus, there are only a few studies that improve and develop monitoring robots through the use of deep learning. For this reason, we aimed to develop a real-time robot monitoring system for the generative growth of tomatoes. The presented method detects tomato fruits grown in hydroponic greenhouses using the Faster R-CNN (region-based convolutional neural network). In addition, we sought to select a color model that was robust to external light, and we used hue values to develop an image-based maturity standard for tomato fruits; furthermore, the developed maturity standard was verified through comparison with expert classification. Finally, the number of tomatoes was counted using a centroid-based tracking algorithm. We trained the detection model using an open dataset and tested the whole system in real-time in a hydroponic greenhouse. A total of 53 tomato fruits were used to verify the developed system, and the developed system achieved 88.6% detection accuracy when completely obscured fruits not captured by the camera were included. When excluding obscured fruits, the system’s accuracy was 90.2%. For the maturity classification, we conducted qualitative evaluations with the assistance of experts.

RUNIMC: An R-based package for imaging mass cytometry data analysis and pipeline validation

We propose a novel pipeline for the analysis of imaging mass cytometry data, comparing an unbiased approach, representing the actual gold standard, with a novel biased method. We made use of both synthetic/ controlled datasets as well as two datasets obtained from FFPE sections of follicular lymphoma, and head and neck patients, stained with a 14 and 29-markers panels respectively. The novel pipeline, denominated RUNIMC, has been completely developed in R and contained in a single package. The novelty resides in the ease with which multi-class random forest classifier can be used to classify image features, making the pathologist and expert classification pivotal, and the use of a random forest regression approach that permits a better detection of cell boundaries, and alleviates the necessity of relying on a perfect nuclear staining.

Investigating the role of Non-Technical Skills in Railway Traffic Operations through Expert Knowledge Elicitation

Many high-risk industries identify non-technical skills as safety-critical abilities of the operational staff that have a protective function against human fallibility. Based on an established non-technical skills classification system, methods for expert knowledge elicitation were used to describe non-technical skills in the specific context of train traffic control in the Netherlands. The findings offer insights regarding the skill importance for good operational outcomes, skill difficulty, categorization, and attitudes based on subject matter experts’ opinions. Substantial overlap between the employed non-technical skills framework and the observed expert classification was found, which might indicate that the experts utilize a mental model of nontechnical skills similar to the one used. Furthermore, considerations concerning the organizational culture and the attitudes towards change provide a promising outlook when introducing novel solutions to non-technical skill training and assessment.

Development and Validation of a Test for the Classification of Horses as Broken or Unbroken

Regulation EC 1/2005 has stricter rules for transportation of unbroken (untamed) vs. broken (tamed) horses, but does not provide adequate tools for their identification. This study aimed to develop and validate such a tool. A behavioural test (Broken/Unbroken Test (BUT)) based on approaching, haltering, and leading was applied to 100 horses. Physiological and additional behavioural data were also collected, and the horses’ status (broken/unbroken) was assessed by the expert who administered the BUT. Each horse’s behaviour during the BUT was scored by four trained observers blinded to the horse’s history. The BUT score showed excellent inter-observer, intra-observer, and test–retest reliability (all intraclass correlation coefficients (ICCs) > 0.75). It was also negatively associated with respiratory rate, avoidance distance, and time needed to approach, halter, and lead the horse (p < 0.05 for all). The optimal BUT score cut-off for discrimination between broken and unbroken horses (gold standard: expert judgment) showed 97.8% sensitivity and 97.3% specificity. There was almost perfect agreement between BUT-based and expert classification of horses (ICC = 0.940). These findings confirm the BUT’s construct and criterion validity. The BUT could provide officials with a feasible, reliable, and valid tool to identify a horse’s broken/unbroken status and, consequently, direct stakeholders towards correct transport procedures.

Diagnosis of Neonatal Late-Onset Infection in Very Preterm Infant: Inter-Observer Agreement and International Classifications

Background: The definition of late-onset bacterial sepsis (LOS) in very preterm infants is not unified. The objective was to assess the concordance of LOS diagnosis between experts in neonatal infection and international classifications and to evaluate the potential impact on heart rate variability and rate of “bronchopulmonary dysplasia or death”. Methods: A retrospective (2017–2020) multicenter study including hospitalized infants born before 31 weeks of gestation with intention to treat at least 5-days with antibiotics was performed. LOS was classified as “certain or probable” or “doubtful” independently by five experts and according to four international classifications with concordance assessed by Fleiss’s kappa test. Results: LOS was suspected at seven days (IQR: 5–11) of life in 48 infants. Following expert classification, 36 of them (75%) were considered as “certain or probable” (kappa = 0.41). Following international classification, this number varied from 13 to 46 (kappa = −0.08). Using the expert classification, “bronchopulmonary dysplasia or death” occurred less frequently in the doubtful group (25% vs. 78%, p < 0.001). Differences existed in HRV changes between the two groups. Conclusion: The definition of LOS is not consensual with a low international and moderate inter-observer agreement. This affects the evaluation of associated organ dysfunction and prognosis.

Fuzzy Boost Classifier of Decision Experts for Multicriteria Group Decision-Making

The expert is a vital role in multicriteria decision-making, which provides source decision opinions. In the existing group decision-making activities, the selection of experts is usually conducted artificially, which relies on personal subjective experience. It has been the urgent demand for an automatic selection of experts, which can help to determine their weights for the follow-up decision calculation. In this paper, an expert classification method is proposed to solve the problem. First, the CatBoost classification algorithm is improved by integrating the 2-tuple linguistic, which can effectively extract the features of samples. Second, the framework of the expert classification is designed. The flow combines the expert resume collection, expert classification, and database update. Third, a decision-making case is analyzed for the expert selection issue. The experiment and result indicate that the proposed classifier performs better than the classic methods. The proposed classification method of the decision experts can support the automatic and intelligent operation of the decision-making activities.