Artificial turf is increasingly being used in the construction of football pitches. One of its characteristics is an infill of sand and rubber granules. At present, different materials and layer thicknesses, as well as grain sizes are used for the sand and mainly for the rubber, but they are chosen with little scientific evidence about their influence on the mechanical and biomechanical properties of the pitch. Based on knowledge from materials science, it is reasonable to suggest that grain morphology may have a large influence on pitch performance. This paper presents research conducted to assess the influence of different parameters related to infill grain morphology on the mechanical properties of artificial turf (force reduction (%), vertical deformation (mm) and vertical ball bounce (m)), as well as on their wear with use, measured according to the F ed eration Internationale de Football Association (FIFA) procedures. The results show a significant reduction of pitch performance with use and a significant influence of grain morphology in mechanical response of artificial turf with respect to impact forces and ball rebound.

BackgroundA sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model.ObjectiveThe aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.MethodsThe algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index) were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.ResultsIn the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.ConclusionAn algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.

Qualitative studies are needed to better understand the attitude of health professionals when prescribing PPIs.

The use of predictive models is becoming widespread. However, these models should be developed appropriately (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies [CHARMS] and Prediction model Risk Of Bias ASsessment Tool [PROBAST] statements). Concerning mortality/recurrence in oropharyngeal cancer, we are not aware of any systematic reviews of the predictive models. We carried out a systematic review of the MEDLINE/EMBASE databases of those predictive models. In these models, we analyzed the 11 domains of the CHARMS statement and the risk of bias and applicability, using the PROBAST tool. Six papers were finally included in the systematic review and all of them presented high risk of bias and several limitations in the statistical analysis. The applicability was satisfactory in five out of six studies. None of the models could be considered ready for use in clinical practice.