2,080 research outputs found
Dihydrocodeine/Agonists for Alcohol Dependents
Objective: Alcohol addiction too often remains insufficiently treated. It shows the same profile as severe chronic diseases, but no comparable, effective basic treatment has been established up to now. Especially patients with repeated relapses, despite all therapeutic approaches, and patients who are not able to attain an essential abstinence to alcohol, need a basic medication. It seems necessary to acknowledge that parts of them need any agonistic substance, for years, possibly lifelong. For >14 years, we have prescribed such substances with own addictive character for these patients. Methods: We present a documented best possible practice, no designed study. Since 1997, we prescribed Dihydrocodeine (DHC) to 102 heavily alcohol addicted patients, later, also Buprenorphine, Clomethiazole (>6 weeks), Baclofen, and in one case Amphetamine, each on individual indication. This paper focuses on the data with DHC, especially. The Clomethiazole-data has been submitted to a German journal. The number of treatments with the other substances is still low. Results: The 102 patients with the DHC treatment had 1367 medically assisted detoxifications and specialized therapies before! The 4 years-retention rate was 26.4%, including 2.8% successfully terminated treatments. In our 12-steps scale on clinical impression, we noticed a significant improvement from mean 3.7 to 8.4 after 2 years. The demand for medically assisted detoxifications in the 2 years remaining patients was reduced by 65.5%. Mean GGT improved from 206.6 U/l at baseline to 66.8 U/l after 2 years. Experiences with the other substances are similar but different in details. Conclusion: Similar to the Italian studies with GHB and Baclofen, we present a new approach, not only with new substances, but also with a new setting and much more trusting attitude. We observe a huge improvement, reaching an almost optimal, stable, long term status in around 1/4 of the patients already. Many further optimizations are possible
A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data
Anomaly detection (AD) tasks have been solved using machine learning
algorithms in various domains and applications. The great majority of these
algorithms use normal data to train a residual-based model, and assign anomaly
scores to unseen samples based on their dissimilarity with the learned normal
regime. The underlying assumption of these approaches is that anomaly-free data
is available for training. This is, however, often not the case in real-world
operational settings, where the training data may be contaminated with a
certain fraction of abnormal samples. Training with contaminated data, in turn,
inevitably leads to a deteriorated AD performance of the residual-based
algorithms.
In this paper we introduce a framework for a fully unsupervised refinement of
contaminated training data for AD tasks. The framework is generic and can be
applied to any residual-based machine learning model. We demonstrate the
application of the framework to two public datasets of multivariate time series
machine data from different application fields. We show its clear superiority
over the naive approach of training with contaminated data without refinement.
Moreover, we compare it to the ideal, unrealistic reference in which
anomaly-free data would be available for training. Since the approach exploits
information from the anomalies, and not only from the normal regime, it is
comparable and often outperforms the ideal baseline as well
Intellectual and motor performance, quality of life and psychosocial adjustment in children with cystinosis
Cystinosis is a rare multisystemic progressive disorder mandating lifelong medical treatment. Knowledge on the intellectual and motor functioning, health-related quality of life and psychosocial adjustment in children with cystinosis is limited. We have investigated nine patients (four after renal transplantation) at a median age of 9.7years (range 5.3-19.9years). Intellectual performance (IP) was analysed with the Wechsler Intelligence Scale for Children-III (seven children) and the Kaufman Assessment Battery for Children (two children). Motor performance (MP) was evaluated using the Zurich Neuromotor Assessment Test, and quality of life (QOL) was studied by means of the Netherlands Organization for Applied Scientific Research Academical Medical Center Child Quality of Life Questionnaire. Psychosocial adjustment was assessed by the Child Behavior Checklist. The overall intelligence quotient (IQ) of our patient cohort (median 92, range 71-105) was significantly lower than that of the healthy controls (p = 0.04), with two patients having an IQ < 85. Verbal IQ (93, range 76-118) was significantly higher than performance IQ (90, range 68-97; p = 0.03). The MP was significantly below the norm for pure motor, pegboard and static balance, as well as for movement quality. The patients' QOL was normal for six of seven dimensions (exception being positive emotions), whereas parents reported significant impairment in positive emotions, autonomy, social and cognitive functions. Significant disturbance was noted in terms of psychosocial adjustment. Based on the results from our small patient cohort, we conclude that intellectual and motor performance, health-related QOL and psychosocial adjustment are significantly impaired in children and adolescents with cystinosi
Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines
Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to considerably improve the fault detection performance compared to the scarce data training. Moreover, it is shown to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine with a large and representative training set. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the cross-turbine scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine
Emergency front-of-neck access in infants: A pragmatic crossover randomized control trial comparing two approaches on a simulated rabbit model.
BACKGROUND
Rapid-sequence tracheotomy and scalpel-bougie tracheotomy are two published approaches for establishing emergency front-of-neck access in infants. It is unknown whether there is a difference in performance times and success rates between the two approaches.
AIMS
The aim of this cross-over randomized control trial study was to investigate whether the two approaches were equivalent for establishing tracheal access in rabbit cadavers. The underlying hypothesis was that the time to achieve the tracheal access is the same with both techniques.
METHODS
Between May and September 2022, thirty physicians (pediatric anesthesiologists and intensivists) were randomized to perform front-of-neck access using one and then the other technique: rapid-sequence tracheotomy and scalpel-bougie tracheotomy. After watching training videos, each technique was practiced four times followed by a final tracheotomy during which study measurements were obtained. Based on existing data, an equivalence margin was set at ∆ = ±10 s for the duration of the procedure. The primary outcome was defined as the duration until tracheal tube placement was achieved successfully. Secondary outcomes included success rate, structural injuries, and subjective participant self-evaluation.
RESULTS
The median duration of the scalpel-bougie tracheotomy was 48 s (95% CI: 37-57), while the duration of the rapid-sequence tracheotomy was 59 s (95% CI: 49-66, p = .07). The difference in the median duration between the two approaches was 11 s (95% CI: -4.9 to 29). The overall success rate was 93.3% (95% CI: 83.8%-98.2%). The scalpel-bougie tracheotomy resulted in significantly fewer damaged tracheal rings and was preferred among participants.
CONCLUSIONS
The scalpel-bougie tracheotomy was slightly faster than the rapid-sequence tracheotomy and favored by participants, with fewer tracheal injuries. Therefore, we propose the scalpel-bougie tracheostomy as a rescue approach favoring the similarity to the adult approach for small children. The use of a comparable equipment kit for both children and adults facilitates standardization, performance, and logistics.
TRIAL REGISTRATION
ClinicalTrials.gov identifier: NCT05499273
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