Diseño y validación de un cuestionario socio-emocional para jóvenes futbolistas de élite. Desing and validation of a socio-emotional questionnaire for yourh footbal players

Validación de un cuestionario socio-emocional en fútbol

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

Deployment and validation of a smart system for screening of language disorders in primary care

Neuro-evolutive development from birth until the age of six years is a decisive factor in a child?s quality of life. Early detection of development disorders in early childhood can facilitate necessary diagnosis and/or treatment. Primary-care pediatricians play a key role in its detection as they can undertake the preventive and therapeutic actions requested to promote a child?s optimal development. However, the lack of time and little specific knowledge at primary-care avoid to applying continuous early-detection anomalies procedures. This research paper focuses on the deployment and evaluation of a smart system that enhances the screening of language disorders in primary care. Pediatricians get support to proceed with early referral of language disorders. The proposed model provides them with a decision-support tool for referral actions to trigger essential diagnostic and/or therapeutic actions for a comprehensive individual development. The research was conducted by starting from a sample of 60 cases of children with language disorders. Validation was carried out through two complementary steps: first, by including a team of seven experts from the fields of neonatology, pediatrics, neurology and language therapy, and, second, through the evaluation of 21 more previously diagnosed cases. The results obtained show that therapist positively accepted the system proposal in 18 cases (86%) and suggested system redesign for single referral to a speech therapist in three remaining cases.

Processing and validation of JEFF3-3.1.2 Cross-section Library into Various Formats: ACE, PENDF, GENDF, MATXSR and BOXER.

Following the processing and validation of JEFF-3.1 performed in 2006 and presented in ND2007, and as a consequence of the latest updated of this library (JEFF-3.1.2) in February 2012, a new processing and validation of JEFF-3.1.2 cross section library is presented in this paper. The processed library in ACE format at ten different temperatures was generated with NJOY-99.364 nuclear data processing system. In addition, NJOY-99 inputs are provided to generate PENDF, GENDF, MATXSR and BOXER formats. The library has undergone strict QA procedures, being compared with other available libraries (e.g. ENDF/B-VII.1) and processing codes as PREPRO-2000 codes. A set of 119 criticality benchmark experiments taken from ICSBEP-2010 has been used for validation purposes.

Validation of an instrument for injury data collection in strength training

Purpose: To provide for the basis for collecting strength training data using a rigorously validated injury report form. Methods: A group of specialist designed a questionnaire of 45 item grouped into 4 dimensions. Six stages were used to assess face, content, and criterion validity of the weight training injury report form. A 13 members panel assessed the form for face validity, and an expert panel assessed it for content and criterion validity. Panel members were consulted until consensus was reached. A yardstick developed by an expert panel using Intraclass correlation technique was used to assess the reability of the form. Test-retest reliability was assessed with the intraclass correlation coefficient (ICC).The strength training injury report form was developed, and the face, content, and criterion validity successfully assessed. A six step protocol to create a yardstick was also developed to assist in the validation process. Both inter-rater and intra rater reliability results indicated a 98% agreement. Inter-rater reliability agreement of 98% for three injuries. Results: The Cronbach?s alpha of the questionnaire was 0.944 (pmenor que0.01) and the ICC of the entire questionnaire was 0.894 (pmenor que0.01). Conclusion: The questionnaire gathers together enough psychometric properties to be considered a valid and reliable tool for register injury data in strength training, and providing researchers with a basis for future studies in this area. Key Words: data collection; validation; injury prevention; strength training

Predicting EQ-5D from the Parkinson's disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers

The impact of the Parkinson's disease and its treatment on the patients' health-related quality of life can be estimated either by means of generic measures such as the european quality of Life-5 Dimensions (EQ-5D) or specific measures such as the 8-item Parkinson's disease questionnaire (PDQ-8). In clinical studies, PDQ-8 could be used in detriment of EQ-5D due to the lack of resources, time or clinical interest in generic measures. Nevertheless, PDQ-8 cannot be applied in cost-effectiveness analyses which require generic measures and quantitative utility scores, such as EQ-5D. To deal with this problem, a commonly used solution is the prediction of EQ-5D from PDQ-8. In this paper, we propose a new probabilistic method to predict EQ-5D from PDQ-8 using multi-dimensional Bayesian network classifiers. Our approach is evaluated using five-fold cross-validation experiments carried out on a Parkinson's data set containing 488 patients, and is compared with two additional Bayesian network-based approaches, two commonly used mapping methods namely, ordinary least squares and censored least absolute deviations, and a deterministic model. Experimental results are promising in terms of predictive performance as well as the identification of dependence relationships among EQ-5D and PDQ-8 items that the mapping approaches are unable to detect